Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=9300
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Raw data are retained for longterm preservation but are not intended for general use." }, { "ob_id": 37504, "uuid": "69bbdb1aedcb40fc9ff3e324b693f8b5", "short_code": "coll", "title": "WesCon: in-situ airborne observations by the FAAM BAE-146 aircraft and Jade-one Dimona aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for WesCon: Wessex summertime convection experiment. \r\nMeteorological in-situ data were collected by a range of instruments on board the FAAM BAe-146 aircraft and the Jade-one Dimona aircraft during a series of flights from May to August 2023 over the southern UK.\r\nThese airborne measurements were made to complement groundbased, sonde and radar observations made for the concurrent WesCon - Observing the Evolving Structures of Turbulence (WOEST) project." } ], "responsiblepartyinfo_set": [ 200225, 200226, 200227, 200228, 200229, 200230, 200231, 200232, 200233, 200234 ], "onlineresource_set": [ 85471, 85472 ] }, { "ob_id": 41176, "uuid": "254dc2c0601e48e39e4fe46fc95822bc", "title": "ICECAPS-ACE: present weather taken at Summit Station Greenland", "abstract": "This dataset contains horizontal optical range and present weather codes at Summit Station, Greenland. Measurements were made using Biral SWS-100 visibility and present weather sensor.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project.\r\n\r\nThese data were continued through the 3 year extension to the ICECAPS-ACE project called ICECAPS-MELT.\r\n\r\nFor these observations hood and window heaters were on continuously and the sensor receiver head was oriented due North.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "asNeeded", "dataLineage": "Data were collected by the ICECAPS project team and deposited at the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ICECAPS-ACE, Aerosol, Cloud, Snow, Temperature", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2026-01-07T08:32:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4064, "bboxName": "Summit Station, Greenland 2022", "eastBoundLongitude": -38.4526934, "westBoundLongitude": -38.4526934, "southBoundLatitude": 72.57859, "northBoundLatitude": 72.57859 }, "verticalExtent": null, "result_field": { "ob_id": 41175, "dataPath": "/badc/icecaps-ace/data/present-weather/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 95275937, "numberOfFiles": 39, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11436, "startTime": "2022-08-01T00:00:00", "endTime": "2025-09-30T23:59:59" }, "resultQuality": { "ob_id": 3444, "explanation": "Data are as given by the data provider with quality control flags defined within each file. No quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-06-24" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41177, "uuid": "4fc50264b5494f359002e4384dd131c1", "short_code": "acq", "title": "ICECAPS-ACE: Summit Aerosol Cloud Experiment: present weather sensor data taken at Summit Station Greenland", "abstract": "ICECAPS-ACE: Summit Aerosol Cloud Experiment: present weather sensor data taken at Summit Station Greenland" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 236 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30502, "uuid": "65eaacda00a244328b944a1b76fbfd4f", "short_code": "proj", "title": "ICECAPS-ACE: Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland - Aerosol Cloud Experiment", "abstract": "Since 2010, the ICECAPS project has been monitoring cloud-atmosphere-energy interactions at Summit Station, in the centre of the Greenland Ice Sheet (GrIS), using a comprehensive suite of ground-based remote sensing instruments and twice daily radiosonde profiles. In 2018, the Aerosol Cloud Experiment (ACE) expansion of ICECAPS saw the addition of a new series of instruments to measure surface aerosol concentrations and turbulent heat fluxes over the ice sheet. Combined with the original ICECAPS instrumentation, the ACE instruments allow for the study of cloud-aerosol-energy interactions over the central GrIS. ICECAPS-ACE is jointly funded by the Natural Environmental Research Council (NERC) and US National Science Foundation (NSF). Award numbers: NERC: NE/S00906X/1. NSF award numbers: 1801318, 1801477, 1801764.\r\n\r\nAdditional data generated as part of ICECAPS-ACE can be accessed at the Arctic Data Center doi:10.18739/A2S17SV6X" }, { "ob_id": 38309, "uuid": "70c96882916149658752b2b123931a5d", "short_code": "proj", "title": "ICECAPS-MELT: NSFGEO-NERC Collaborative Research", "abstract": "Overview: A three-year extension to the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit (ICECAPS) project. This project was an international collaboration that funded the original ICECAPS researchers through the U.S. National Science Foundation's Arctic Observing Network and a team of researchers at the University of Leeds through the U.K. Natural Environment Research Council. The ICECAPS project continuously operated a sophisticated suite of ground-based instruments at Summit Station, Greenland since 2010 for observation of clouds, precipitation, and atmospheric structure. The project significantly advanced the understanding of cloud properties, radiation and surface energy, and precipitation processes over the Greenland Ice Sheet (GrIS) during a period of rapid climate change. The project supported numerous national and international collaborations in process-based model evaluation, development of new measurement techniques, ground comparisons for multiple satellite measurements and aircraft missions, and operational radiosonde data for weather forecast models. The project proposed to complement the ICECAPS Summit observatory by building, testing and deploying an additional observatory for measuring parameters of the surface mass and energy budgets of the GrIS. The observatory takes a novel approach for unattended, autonomous operations by supporting a suite of instruments that require moderate power and manageable internet bandwidth. The new observatory was deployed in successive summers at Summit Station in the dry-snow zone and at Dye-2 in the percolation zone. If this pilot project is successful, a network of these observatories will be proposed for future deployment that will observe the processes of air parcels as they move along Lagrangian trajectories in southwestern Greenland.\r\n\r\nGrant award: NE/X002403/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 53936, 53937, 53938, 53941, 53942, 53943, 53944, 62252, 62253, 74110, 75561, 75562, 75563, 75564 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30507, "uuid": "f06c6aa727404ca788ee3dd0515ea61a", "short_code": "coll", "title": "ICECAPS-ACE: Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland - Aerosol Cloud Experiment measurements", "abstract": "This dataset collection contains in situ atmospheric and aerosol measurements collected at Summit Station, Greenland.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project. Since 2010, the ICECAPS project has been monitoring cloud-atmosphere-energy interactions at Summit Station, in the centre of the Greenland Ice Sheet (GrIS), using a comprehensive suite of ground-based remote sensing instruments and twice daily radiosonde profiles. In 2018, the Aerosol Cloud Experiment (ACE) expansion of ICECAPS saw the addition of a new series of instruments to measure surface aerosol concentrations and turbulent heat fluxes over the ice sheet. Combined with the original ICECAPS instrumentation, the ACE instruments allow for the study of cloud-aerosol-energy interactions over the central GrIS.\r\n\r\nThis dataset collection contains the measurements collected as part of the ACE component of ICECAPS-ACE, which includes the following:\r\n1) Surface-temperature-profile: A near surface temperature profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n2) Surface-moisture-profile: A near surface moisture profile from four temperature/ humidity sensors distributed on the 15 m tower at Summit.\r\n3) Surface-winds-profile: A near surface wind profile from four sonic anemometers distributed on the 15 m tower at Summit.\r\n4) Snow-height: The distance to the snow surface from the lowest level of instruments on the 15 m tower at Summit, detected by a sonic-ranging sensor.\r\n5) Skin-temperature: The brightness temperature of the snow surface as detected by an infrared radiation thermometer.\r\n6) Aerosol-concentration: The concentration of condensation nuclei (> 5nm diameter) measured at the surface using a Condensation Particle Counter.\r\n7) Aerosol-size-distribution: The size-resolved concentration of surface aerosol particles between 0.25 and 6.5 um in diameter measured using an Optical Particle Counter.\r\n8) Flux-components: High resolution temperature, humidity and wind fluctuations that can be used to estimate turbulent fluxes using eddy covariance, located at two levels on the 15 m tower at Summit.\r\n9) Flux-estimates: Estimates of turbulent heat and momentum fluxes by applying the eddy covariance technique to flux-components.\r\n\r\nOther ICECAPS data are available here:\r\nhttps://psl.noaa.gov/arctic/observatories/summit/\r\n\r\nFrom August 2022 to August 2025, these measurements were supported by the ICECAPS-MELT project (Measurements along a Transect)." } ], "responsiblepartyinfo_set": [ 200245, 200241, 200242, 200243, 200246, 200244, 200247, 200248, 200249, 200250, 200251, 216406 ], "onlineresource_set": [ 85473, 85474, 85475, 85476, 85477 ] }, { "ob_id": 41180, "uuid": "bc5b7568ef2a467fa97642910eb45aa7", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 23.08", "abstract": "This version 23.08 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 54\r\n88/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. The core DECC network\r\n consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to\r\n assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-09-11T13:12:10", "updateFrequency": "notPlanned", "dataLineage": "Data were collected using in situ trace gas analysers. Data quality assurance and quality control is carried out regularly by each station PI, and overall DECC Network data reviews are conducted every 2 months. Data are traceable to international calibration scales. Data were collected by the DECC network team and deposited at the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "UK-DECC, trace gases", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-06-28T15:33:59", "doiPublishedTime": "2024-08-23T09:19:29", "removedDataTime": null, "geographicExtent": { "ob_id": 4065, "bboxName": "UK DECC network", "eastBoundLongitude": 1.1387, "westBoundLongitude": -2.53992, "southBoundLatitude": 50.97675, "northBoundLatitude": 54.35861 }, "verticalExtent": null, "result_field": { "ob_id": 41181, "dataPath": "/badc/uk-decc-network/data/v23.08", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 726009099, "numberOfFiles": 132, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11865, "startTime": "2012-02-23T00:00:00", "endTime": "2023-07-31T23:59:59" }, "resultQuality": { "ob_id": 4465, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data An alysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-12-01" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41182, "uuid": "34ce623def704473a1736b0e79d5de12", "short_code": "acq", "title": "UK-DECC trace species measurements at UK-DECC network sites V23.08", "abstract": "UK-DECC trace species measurements at UK-DECC network sites including Ridge Hill Tall Tower, Bilsdale Tall Tower, Heathfield Tall Tower and Tacolneston Tall Tower." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 27561, "uuid": "081a5ec3884441398aa2daae53a6189b", "short_code": "proj", "title": "UK DECC (Deriving Emissions linked to Climate Change) Network", "abstract": "The core UK Deriving Emissions linked to Climate Change (DECC) Network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet within 10 metres of ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. High frequency measurements of all major greenhouse gases are made at the four UK stations, including carbon dioxide, methane, nitrous oxide and sulfur hexafluoride. \r\n\r\nData from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks. This work is funded by the UK Government Department for Energy Security and Net Zero (DESNZ) under contracts TRN1028/06/2015, TRN1537/06/2018, TRN5488/11/2021 and and prj_1604 to the University of Bristol and through the National Measurement System at the National Physical Laboratory." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 58411, 58412, 58413, 58414, 58415, 58416, 61969, 74599, 74600, 74601, 74602, 74603, 74604, 74605, 74606, 74607, 74608, 74609, 74610, 74611, 74612, 74613, 74614, 74615, 74616, 74617, 74618, 74619, 74620, 74621, 74622, 74623, 74624, 74625, 74626, 74627, 74628, 74629, 74630, 74631, 74632, 74633, 74634, 74635, 74636, 74637, 74638, 74639, 74640, 74641, 75516, 75517, 75518, 75519, 75520, 75521, 75522, 75523, 75524, 75525, 75526, 75527, 75528, 75529, 75530, 75531, 75532, 75533, 75534, 75535, 75536, 75537, 75538, 75539, 75540, 75541, 75542, 75543, 75544, 75545, 75546, 75547, 75548, 75549, 75550, 75551, 75552, 75553, 75554, 75555, 75556, 75557, 75558, 75559, 75560, 82253, 82254, 82255, 82256, 82257, 82258, 82259, 82260, 82261, 82262, 82263, 82264, 82265, 82266, 82267, 82268, 82269, 82270, 82271, 82272, 82273, 82274, 82275, 82276, 82277, 82278, 82279, 82280, 82281, 82282, 82283, 82284, 82285, 82286, 82287, 82288, 82289, 82290, 82291, 82292, 82293, 82294, 82295, 82296, 82297, 82298, 82299, 82300, 82301, 82302, 82303, 82304, 82305, 82306, 82307, 82308, 82309, 82310, 82311, 82312, 82313, 82314, 82315, 82316, 82317, 82318, 82319, 82320, 82321, 82322, 82323, 82324, 82325, 82326, 82327, 82328, 82329, 82330, 82331, 82332 ], "vocabularyKeywords": [], "identifier_set": [ 13177 ], "observationcollection_set": [ { "ob_id": 27499, "uuid": "f5b38d1654d84b03ba79060746541e4f", "short_code": "coll", "title": "UK DECC (Deriving Emissions linked to Climate Change) Network", "abstract": "This dataset collection consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN1028/06/2015, TRN1537/06/2018, TRN5488/11/2021 and prj_1604) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\n\r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet within 10 metres of ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." } ], "responsiblepartyinfo_set": [ 200256, 200260, 200257, 200258, 200259, 200261, 200262, 200263, 200277, 205087, 200269, 200273, 200268, 205088, 200270, 200279, 205089, 200264, 200275, 200265, 200267, 200271, 200276, 200266 ], "onlineresource_set": [ 85478, 85479 ] }, { "ob_id": 41194, "uuid": "a45e0dad6eb1438cac07b38b13b7dcc0", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output for the \"hist-totalO3\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output for the \"historical total ozone-only run\" (hist-totalO3) experiment. These are available at the following frequencies: Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon and day. The runs included the ensemble members: r11i1p1f3, r12i1p1f3, r13i1p1f3, r14i1p1f3, r15i1p1f3, r16i1p1f3, r17i1p1f3, r18i1p1f3, r19i1p1f3, r20i1p1f3, r21i1p1f3, r22i1p1f3, r23i1p1f3, r24i1p1f3, r25i1p1f3, r26i1p1f3, r27i1p1f3, r28i1p1f3, r29i1p1f3 and r30i1p1f3.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2023-12-04T08:57:24.111541", "lastUpdatedDate": "2023-12-04T08:57:24.111561", "latestDataUpdateTime": "2025-03-31T20:07:22", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Met Office Hadley Centre (MOHC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-LL, hist-totalO3, Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon, day", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-12-04T08:57:24.307172", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41195, "dataPath": "/badc/cmip6/data/CMIP6/DAMIP/MOHC/HadGEM3-GC31-LL/hist-totalO3", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8473374928357, "numberOfFiles": 9301, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11438, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-30T12:00:00" }, "resultQuality": { "ob_id": 3334, "explanation": "The CMIP6 data are checked by CEDA to ensure that they conform to the CMOR metadata standards (https://cmor.llnl.gov) Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - primary data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41196, "uuid": "449c965c9157499499a4803580d36220", "short_code": "comp", "title": "Met Office Hadley Centre (MOHC) running: experiment hist-totalO3 using the HadGEM3-GC31-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"historical total ozone-only run\" (hist-totalO3) experiment using the HadGEM3-GC31-LL model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28398, "uuid": "04d56adf2c3247fc89af80c8f4bdb41b", "short_code": "proj", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Met Office Hadley Centre (MOHC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 27828, 27829, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50468, 50475, 50481, 50496, 50498, 50509, 50542, 50543, 50549, 50554, 50555, 50557, 50564, 50566, 50567, 50568, 50569, 50570, 50571, 50572, 50575, 50578, 50579, 50580, 50581, 50583, 50586, 50587, 50588, 50589, 50591, 50592, 50596, 50597, 50598, 50603, 50605, 50608, 50609, 50610, 50611, 50612, 50614, 50615, 50616, 50617, 50621, 52755, 53111, 53128, 53129, 53134, 54212, 54218, 54235, 54236, 54237, 54246, 54247, 54253, 54255, 54258, 54262, 54264, 54282, 54299, 54306, 54313, 54314, 54350, 54460, 54462, 54468, 54476, 54483, 54492, 60438, 62736, 71572, 71573, 71574, 71615, 75345 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28749, "uuid": "014568b39a754538a27e25f24e7f1fca", "short_code": "coll", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 200295, 200296, 200297, 200298, 200299, 200300, 200301, 200303, 200304, 200302 ], "onlineresource_set": [ 85501, 85503, 85505, 85482, 85483, 85485, 85487, 85489, 85491, 85493, 85495, 85497, 85499, 85507, 85509, 85511, 85513, 85515, 85517, 85519, 85521, 85523, 85525, 85526, 85527 ] }, { "ob_id": 41197, "uuid": "250226f551d14f0586456b37620f21d0", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output for the \"hist-volc\" experiment", "abstract": "The World Climate Research Program (WCRP) Coupled Model Intercomparison Project, Phase 6 (CMIP6) data from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output for the \"historical volcanic-only run\" (hist-volc) experiment. These are available at the following frequencies: Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon and day. The runs included the ensemble members: r11i1p1f3, r12i1p1f3, r13i1p1f3, r14i1p1f3, r15i1p1f3, r16i1p1f3, r17i1p1f3, r18i1p1f3, r19i1p1f3 and r20i1p1f3.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2023-12-04T09:08:53.772534", "lastUpdatedDate": "2023-12-04T09:08:53.772548", "latestDataUpdateTime": "2025-04-09T12:27:10", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Met Office Hadley Centre (MOHC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-LL, hist-volc, Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon, day", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-12-04T09:08:53.991230", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41198, "dataPath": "/badc/cmip6/data/CMIP6/DAMIP/MOHC/HadGEM3-GC31-LL/hist-volc", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8473358209314, "numberOfFiles": 9301, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11439, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-30T12:00:00" }, "resultQuality": { "ob_id": 3334, "explanation": "The CMIP6 data are checked by CEDA to ensure that they conform to the CMOR metadata standards (https://cmor.llnl.gov) Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - primary data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41199, "uuid": "14b68a8d460842bea039f732d7bf8284", "short_code": "comp", "title": "Met Office Hadley Centre (MOHC) running: experiment hist-volc using the HadGEM3-GC31-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"historical volcanic-only run\" (hist-volc) experiment using the HadGEM3-GC31-LL model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28398, "uuid": "04d56adf2c3247fc89af80c8f4bdb41b", "short_code": "proj", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Met Office Hadley Centre (MOHC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 27828, 27829, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50468, 50475, 50481, 50496, 50498, 50509, 50542, 50543, 50549, 50554, 50555, 50557, 50564, 50566, 50567, 50568, 50569, 50570, 50571, 50572, 50575, 50578, 50579, 50580, 50581, 50583, 50586, 50587, 50588, 50589, 50591, 50592, 50596, 50597, 50598, 50603, 50605, 50608, 50609, 50610, 50611, 50612, 50614, 50615, 50616, 50617, 50621, 52755, 53111, 53128, 53129, 53134, 54212, 54218, 54235, 54236, 54237, 54246, 54247, 54253, 54255, 54258, 54262, 54264, 54282, 54299, 54306, 54313, 54314, 54350, 54460, 54462, 54468, 54476, 54483, 54492, 60438, 62736, 71572, 71573, 71574, 71615, 75345 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28749, "uuid": "014568b39a754538a27e25f24e7f1fca", "short_code": "coll", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 200310, 200311, 200312, 200313, 200314, 200315, 200316, 200318, 200319, 200317 ], "onlineresource_set": [ 85530, 85531, 85533, 85535, 85537, 85539, 85541, 85543, 85545, 85547, 85549, 85551, 85553, 85554, 85555 ] }, { "ob_id": 41205, "uuid": "c5f0aa806ec444b4a4209b49efc4bb65", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Nadir radar altimeters Water Level product, v1.1", "abstract": "This dataset contains water level (WL) data from the ESA Climate Change Initiative River Discharge project (RD_cci). Water level in this context corresponds to the distance between river surface water and a reference surface (the WGS84 ellipsoid). This physical variable might also be referred to as Water Surface Elevation (WSE) in other dataset or publications.\r\n\r\n These river water level time series have been computed in at 54 locations (within 18 river basins). The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6). At each location, time series are provided for each available single nadir radar altimetry mission. Based on these single mission time series, merged multi-missions WL time series have also been produced.", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": "2024-03-16T01:51:48", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CCI, Water Level, River Discharge", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-03-15T15:32:56", "doiPublishedTime": "2024-03-15T17:22:38", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41460, "dataPath": "/neodc/esacci/river_discharge/data/WL/v1.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 35070742, "numberOfFiles": 747, "fileFormat": "Data are provided in both CSV and NetCDF" }, "timePeriod": { "ob_id": 11558, "startTime": "1992-12-22T00:00:00", "endTime": "2023-09-18T23:59:59" }, "resultQuality": { "ob_id": 4524, "explanation": "For information on the data quality see the related documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41467, "uuid": "b9ff64ed90124634bd9098c18d22c0ae", "short_code": "cmppr", "title": "Composite process for ESA River Discharge Climate Change Initiative (RD_cci): Water Level product, v1.1", "abstract": "The data has been derived from nadir viewing satellite radar altimeter missions (ERS-2, Envisat, Saral, Topex-Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-3A/B and Sentinel 6)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2612, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 70, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_river_discharge_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41537, "uuid": "dbba9cfe8d104648b19e39f4c2da1a27", "short_code": "proj", "title": "ESA River Discharge Climate Change Initiative (RD_cci)", "abstract": "The ESA river discharge Climate Change Initiative project aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63576, 75511, 75512, 75513, 75514, 75515 ], "vocabularyKeywords": [], "identifier_set": [ 12851 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 200338, 200339, 200340, 200341, 200342, 200343, 202210, 202117, 202211, 202212, 202213, 202214, 202215, 202216, 202217, 202218 ], "onlineresource_set": [ 86064, 86065, 86117, 86118, 86119 ] }, { "ob_id": 41207, "uuid": "b94cbe2ae4bf45cfa8dc58e98170c07c", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10", "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 15th November 2021. There are minor interruptions during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-10-31T02:03:34", "updateFrequency": "notPlanned", "dataLineage": "The data record has been produced by the University of Leicester working within the ESA Land Surface Temperature Climate Change Initiative (LST_cci), and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).\r\nContact: data.lst-cci@acri-st.fr", "removedDataReason": "", "keywords": "land surface temperature, ESA, CCI, AVHRR Metop-A", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.01 degree", "status": "completed", "dataPublishedTime": "2024-06-20T08:11:51", "doiPublishedTime": "2024-06-20T15:03:50", "removedDataTime": null, "geographicExtent": { "ob_id": 2896, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41688, "dataPath": "/neodc/esacci/land_surface_temperature/data/METOPA_AVHRR/L3C/0.01/v1.10/daily/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6440266929499, "numberOfFiles": 10617, "fileFormat": "NetCDF format" }, "timePeriod": { "ob_id": 11685, "startTime": "2007-03-01T00:00:00", "endTime": "2021-11-15T23:59:59" }, "resultQuality": { "ob_id": 3798, "explanation": "For information on the data quality see the associated LST_cci documentation", "passesTest": true, "resultTitle": "LST cci", "date": "2021-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41689, "uuid": "595342f435e145d5adea62b12a4297de", "short_code": "cmppr", "title": "Composite process for the ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10", "abstract": "Data has been derived from the Advanced Very High Resolution Radiometer 3(AVHRR-3) on the Metop-A satellite. \r\n\r\nFor more information on the retrieval algorithm used see the documentation on the LST CCI webpage: https://climate.esa.int/sites/default/files/LST-CCI-D2.2-ATBD%20-%20i1r1%20-%20Algorithm%20Theoretical%20Basis%20Document.pdf." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2558, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 30, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_lst_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 33361, "uuid": "555149fdc3ef4e23a1de8ece93c29f5d", "short_code": "proj", "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci)", "abstract": "The land surface temperature (LST) CCI project, which is funded by the European Space Agency (ESA) as part of the Agency’s Climate Change Initiative (CCI) Programme, aims to deliver a significant improvement on the capability of current satellite LST data records to meet the challenging Global Climate Observing System (GCOS) requirements for climate applications to realise the full potential of long-term LST data for climate science.\r\n\r\nAccurate knowledge of LST plays a key role in describing the physics of land-surface processes at regional and global scales as they combine information on both the surface-atmosphere interactions and energy fluxes within the Earth Climate System. LST provides a metric of surface state when combined with vegetation parameters and soil moisture and is one of the drivers of vegetation phenology. Furthermore, LST is an independent temperature data set for quantifying climate change complementary to the near-surface air temperature ECV based on in situ measurements and reanalyses.\r\n\r\nThe team uses data from a variety of satellites to provide an accurate view of temperatures across land surfaces globally over the past +20 years. This involves developing innovative techniques to merge data from different satellites into combined long-term satellite records for climate. These will all be evaluated by scientists working at leading climate centres." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62778, 62779, 62780, 62781, 62790, 62791, 62792, 66303, 66305, 66308, 66310, 66311, 66312, 66751, 66754, 75124 ], "vocabularyKeywords": [], "identifier_set": [ 13140 ], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." } ], "responsiblepartyinfo_set": [ 200356, 200357, 200358, 200359, 200352, 200353, 200354, 200355, 200360, 200361, 200362 ], "onlineresource_set": [ 85586, 86306, 86309, 86307, 86308 ] }, { "ob_id": 41222, "uuid": "d77400c037da4416b44ec3f86695d32c", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): control data produced by the NAVGEM model at NRL", "abstract": "This dataset contains model data for SNAPSI experiment 'control' produced by scientists at NRL (Naval Research Laboratory, Washington DC, USA). This dataset contains all ensemble members produced by the NRL NAVGEM model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe control experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the time-evolving climatological state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-03-08T01:58:05", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Naval Research Laboratory (NRL) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "control, NRL, NAVGEM, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2025-03-07T18:59:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 41223, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NRL/NAVGEM/control", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3185389487932, "numberOfFiles": 12481, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41224, "uuid": "366e9234490b4c80ba4695144fe2e1b7", "short_code": "comp", "title": "NRL NAVGEM model", "abstract": "The underlying data was produced by the NRL NAVGEM model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50419, 50426, 50427, 50429, 50431, 50475, 50481, 50496, 50498, 50549, 50559, 50561, 50566, 50579, 50597, 52755, 54228, 62501, 62560, 62561, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74365, 74366, 74367 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41230, "uuid": "ecad5261e8d446bfa842b4b7a227d5b0", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the NAVGEM model at NRL", "abstract": "The NAVGEM model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the Naval Research Laboratory (NRL), Washington DC, USA.\r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)" } ], "responsiblepartyinfo_set": [ 200386, 200388, 200387, 200389, 200390, 200391, 200392, 200393 ], "onlineresource_set": [ 85599, 85600, 85601, 85602, 85603 ] }, { "ob_id": 41225, "uuid": "05d63906a1ce48dab130f128b404679c", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): free data produced by the NAVGEM model at NRL", "abstract": "This dataset contains model data for SNAPSI experiment 'free' produced by scientists at NRL (Naval Research Laboratory, Washington DC, USA). This dataset contains all ensemble members produced by the NRL NAVGEM model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe free experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-03-08T01:58:06", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Naval Research Laboratory (NRL) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "free, NRL, NAVGEM, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2025-03-07T19:00:30", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 41226, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NRL/NAVGEM/free", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3179247313795, "numberOfFiles": 12481, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41224, "uuid": "366e9234490b4c80ba4695144fe2e1b7", "short_code": "comp", "title": "NRL NAVGEM model", "abstract": "The underlying data was produced by the NRL NAVGEM model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50426, 50429, 50431, 50475, 50481, 50496, 50498, 50559, 50561, 50579, 52755, 54228, 62501, 62560, 62561, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74365, 74366, 74367 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41230, "uuid": "ecad5261e8d446bfa842b4b7a227d5b0", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the NAVGEM model at NRL", "abstract": "The NAVGEM model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the Naval Research Laboratory (NRL), Washington DC, USA.\r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)" } ], "responsiblepartyinfo_set": [ 200401, 200402, 200403, 200404, 200405, 200406, 200407, 200408 ], "onlineresource_set": [ 85607, 85608, 85604, 85605, 85606 ] }, { "ob_id": 41227, "uuid": "b3353ace59bc4ca7b94f38f4b0510448", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): nudged data produced by the NAVGEM model at NRL", "abstract": "This dataset contains model data for SNAPSI experiment 'nudged' produced by scientists at NRL (Naval Research Laboratory, Washington DC, USA). This dataset contains all ensemble members produced by the NRL NAVGEM model.\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\nThe nudged experiment is a set of retrospective, 45-day, 50-member ensemble forecasts. Following the initial date, the stratospheric zonal mean temperatures and zonal winds are nudged towards the observed time-evolving state. The forecasts are initialized on the date indicated by the sub-experiment id; for instance, the sub-experiment 's20180125' is initialized on 25 January 2018. The ocean, sea-ice, land-surface and ozone are all initialized and run prognostically.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2023-02-07T16:04:12", "latestDataUpdateTime": "2025-03-08T01:58:05", "updateFrequency": "", "dataLineage": "Data were produced by scientists at the Naval Research Laboratory (NRL) and published by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "nudged, NRL, NAVGEM, stratosphere, arctic, antarctic, stratospheric polar vortex, SNAPSI, SNAP, APARC", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "1x1 degree", "status": "ongoing", "dataPublishedTime": "2025-03-07T19:00:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": { "ob_id": 249, "highestLevelBound": 40.0, "lowestLevelBound": 100000.0, "units": "Pa" }, "result_field": { "ob_id": 41228, "dataPath": "/badc/snap/data/post-cmip6/SNAPSI/NRL/NAVGEM/nudged", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3183078808573, "numberOfFiles": 12481, "fileFormat": "Files are Net-CDF formatted" }, "timePeriod": { "ob_id": 10988, "startTime": "2018-01-25T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 3916, "explanation": "Quality control checks performed at CEDA confirmed that the data meets the SNAPSI metadata requirements.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-03-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41224, "uuid": "366e9234490b4c80ba4695144fe2e1b7", "short_code": "comp", "title": "NRL NAVGEM model", "abstract": "The underlying data was produced by the NRL NAVGEM model for the SNAPSI project." }, "procedureCompositeProcess": null, "imageDetails": [ 229 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2560, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 56, "licenceURL": "https://creativecommons.org/licenses/by-sa/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37088, "uuid": "0a5a1ce22fb047749e040879efa8e9b5", "short_code": "proj", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)", "abstract": "SNAPSI is a model intercomparison protocol designed to study the role of the Arctic and Antarctic stratospheric polar vortices in sub-seasonal to seasonal forecast models. Based on a set of controlled, sub-seasonal, ensemble forecasts of three recent events, the protocol aims to address four main scientific goals. \r\nFirst, to quantify the impact of improved stratospheric forecasts on near-surface forecast skill. \r\nSecond, to attribute specific extreme events to stratospheric variability. \r\nThird, to assess the mechanisms by which the stratosphere influences the troposphere in the forecast models, \r\nand fourth, to investigate the wave processes that lead to the stratospheric anomalies themselves. \r\nAlthough not a primary focus, the experiments are furthermore expected to shed light on coupling between the tropical stratosphere and troposphere. \r\nThe output requested will allow for a more detailed, process-based community analysis than has been possible with existing databases of sub-seasonal forecasts." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 50419, 50426, 50427, 50429, 50431, 50475, 50481, 50496, 50498, 50549, 50559, 50561, 50566, 50579, 50597, 52755, 54228, 62501, 62560, 62561, 64080, 66075, 66076, 66077, 66082, 71614, 71619, 71634, 74365, 74366, 74367 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41230, "uuid": "ecad5261e8d446bfa842b4b7a227d5b0", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the NAVGEM model at NRL", "abstract": "The NAVGEM model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the Naval Research Laboratory (NRL), Washington DC, USA.\r\n\r\n\r\nThe SNAPSI project is a model intercomparison project to study the role of the stratosphere in subseasonal forecasts following stratospheric sudden warmings and the representation of stratosphere-troposphere coupling in subseasonal forecast models.\r\n\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n\r\n- Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): A Protocol for Investigating the Role of the Stratospheric Polar Vortex in Subseasonal to Seasonal Forecasts\r\n- New set of controlled numerical experiments: Stratospheric Nudging And Predictable Surface Impacts (SNAPSI)\r\n- NAVGEM model reference publications: Hogan et al. (2014); McCormack et al. (2017); Eckermann et al. (2018)" } ], "responsiblepartyinfo_set": [ 200409, 200410, 200411, 200412, 200413, 200414, 200415, 200416 ], "onlineresource_set": [ 85609, 85613, 85612, 85610, 85611 ] }, { "ob_id": 41231, "uuid": "471698c7db45451cbf3b7d834ecab9fd", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly sea surface salinity product for the Northern Hemisphere on a 25km EASE grid, v04.41, for 2010 to 2022", "abstract": "This dataset contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It is spatially sampled on a NH polar 25km EASE (Equal Area Scalable Earth) grid with 1 day of time sampling. This product is also available separately on a regular lat/lon grid. A monthly product is also available. In addition to salinity, information on uncertainties are provided. For more information see the user guide and other product documentation available from the linked Sea Surface Salinity CCI web page.\r\n\r\nCompared to version 3.21 of the data, version 04.41 SSS is of similar or improved quality. The main improvements concern high latitude regions (reduced seasonal biases and better ice flagging). The v04.41 dataset covers a longer period (Jan 2010-Oct 2022).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:21:56", "updateFrequency": "notPlanned", "dataLineage": "Data have been produced by the Sea Surface Salinity project as part of the ESA Climate Change Initiative programme and delivered for long-term archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA Climate Change Initiative, CCI, Sea Surface Salinity", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-16T10:47:17", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4150, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 45.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41484, "dataPath": "/neodc/esacci/sea_surface_salinity/data/v04.41/NHv4.41/7days/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9890019556, "numberOfFiles": 4686, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 11444, "startTime": "2010-01-02T00:00:00", "endTime": "2023-12-31T23:59:59" }, "resultQuality": { "ob_id": 3530, "explanation": "Data are as provided by the CCI Sea Surface Salinity project team. Estimates of uncertainty are provided in the data files, see documentation for further information", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-09-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 29942, "uuid": "31c2d365861f4a7f87a56a0178321034", "short_code": "cmppr", "title": "CCI Sea Surface Salinity retrieval process", "abstract": "The ESA CCI Climate Change Initiate Sea Surface Salinity project has derived salinity data from the SMOS, SMAP and Aquarius Satellite instruments." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2550, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 43, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_salinity_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 28235, "uuid": "d64aaf9ca941488d90ba0ad3e339977d", "short_code": "proj", "title": "ESA Sea Surface Salinity Climate Change Initiative", "abstract": "The ESA Sea Surface Salinity (SSS) Climate Change Initiative project is part of the European Space Agency's Climate Change Initiative programme. It aims to provide data products that are specifically adapted to climate applications (i.e. include information on accuracy and uncertainty within the data). Furthermore, this project will explore the need to improve the performance of current SSS algorithm retrievals and directly contribute to climate science studies submitted to the next International Panel on Climate Change (IPCC) Annual Review for climate change in 2020." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 54768, 54769, 54771, 60438, 74953, 74954, 74955, 74956, 74957 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41237, "uuid": "f2ca631f29a24c47a7e98654ddf2c7d9", "short_code": "coll", "title": "ESA Sea Surface Salinity Climate Change Initiative (Sea_Surface_Salinity_cci): Weekly and monthly sea surface salinity products, v04.41, for 2010 to 2022", "abstract": "The European Space Agency (ESA) Sea Surface Salinity Climate Change Initiative (CCI) consortium has produced global, level 4, multi-sensor Sea Surface Salinity maps covering the 2010-2022 period.\r\n\r\nThis dataset collection contains Sea Surface Salinity (SSS) v04.41 data at a spatial resolution of 50km and a time resolution of 1 week. It has been spatially sampled on a 0.25° grid and 1 day of time sampling. This product is also available on polar 25 km EASE (Equal Area Scalable Earth) grids.\r\n\r\nA monthly product is also available, at a spatial resolution of 25 km and a time resolution of 1 month. It is spatially sampled on a 0.25° grid and 15 days of time sampling. This product is also available on polar 25km EASE grids.\r\n\r\nIn addition to salinity, information on uncertainties are provided. 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These are available at the following frequencies: Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon and day. The runs included the ensemble members: r11i1p1f3, r12i1p1f3, r13i1p1f3, r14i1p1f3, r15i1p1f3, r16i1p1f3, r17i1p1f3, r18i1p1f3, r19i1p1f3, r20i1p1f3, r21i1p1f3, r22i1p1f3, r23i1p1f3, r24i1p1f3, r25i1p1f3, r26i1p1f3, r27i1p1f3, r28i1p1f3, r29i1p1f3, r30i1p1f3, r31i1p1f3, r32i1p1f3, r33i1p1f3, r34i1p1f3, r35i1p1f3, r36i1p1f3, r37i1p1f3, r38i1p1f3, r39i1p1f3, r40i1p1f3, r41i1p1f3, r42i1p1f3, r43i1p1f3, r44i1p1f3, r45i1p1f3, r46i1p1f3, r47i1p1f3, r48i1p1f3, r49i1p1f3, r50i1p1f3, r51i1p1f3, r52i1p1f3, r53i1p1f3, r54i1p1f3, r55i1p1f3, r56i1p1f3, r57i1p1f3, r58i1p1f3, r59i1p1f3 and r60i1p1f3.\n\nCMIP6 was a global climate model intercomparison project, coordinated by PCMDI (Program For Climate Model Diagnosis and Intercomparison) on behalf of the WCRP and provided input for the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report (AR6).\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record.", "creationDate": "2024-01-02T08:40:19.531059", "lastUpdatedDate": "2024-01-02T08:40:19.531077", "latestDataUpdateTime": "2025-03-13T17:30:52", "updateFrequency": "asNeeded", "dataLineage": "Data were produced and verified by Met Office Hadley Centre (MOHC) scientists before publication via the Earth Systems Grid Federation (ESGF) and a copy obtained by the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "CMIP6, WCRP, climate change, MOHC, HadGEM3-GC31-LL, hist-sol, Amon, Emon, EmonZ, LImon, Lmon, Omon, SImon, day", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-01-02T08:40:19.687790", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41260, "dataPath": "/badc/cmip6/data/CMIP6/DAMIP/MOHC/HadGEM3-GC31-LL/hist-sol", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8473224236294, "numberOfFiles": 9301, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11456, "startTime": "1850-01-01T12:00:00", "endTime": "2020-12-30T12:00:00" }, "resultQuality": { "ob_id": 3334, "explanation": "The CMIP6 data are checked by CEDA to ensure that they conform to the CMOR metadata standards (https://cmor.llnl.gov) Where any CMIP6 data are identified as having a quality issue this is recorded in the CMIP6 errata service: https://errata.es-doc.org/static/index.html", "passesTest": true, "resultTitle": "CMIP6 Quality Statement - primary data", "date": "2017-02-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41261, "uuid": "0ee4dc348b3943babe9b19b5a4bf865f", "short_code": "comp", "title": "Met Office Hadley Centre (MOHC) running: experiment hist-sol using the HadGEM3-GC31-LL model.", "abstract": "Met Office Hadley Centre (MOHC) running the \"historical solar-only run\" (hist-sol) experiment using the HadGEM3-GC31-LL model. See linked documentation for available information for each component." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2520, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 1, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/CMIP6_Terms_of_Use.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 28398, "uuid": "04d56adf2c3247fc89af80c8f4bdb41b", "short_code": "proj", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) contribution", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 contribution to the project by the Met Office Hadley Centre (MOHC) team." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 27828, 27829, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50468, 50475, 50481, 50486, 50496, 50498, 50509, 50542, 50543, 50549, 50554, 50555, 50557, 50564, 50566, 50567, 50568, 50569, 50570, 50571, 50572, 50575, 50578, 50579, 50580, 50581, 50583, 50586, 50587, 50588, 50589, 50591, 50592, 50596, 50597, 50598, 50603, 50605, 50608, 50609, 50610, 50611, 50612, 50614, 50615, 50616, 50617, 50621, 51210, 52755, 53111, 53128, 53129, 53134, 54212, 54218, 54235, 54236, 54237, 54246, 54247, 54253, 54255, 54258, 54262, 54264, 54282, 54299, 54306, 54313, 54314, 54350, 54460, 54462, 54468, 54476, 54483, 54492, 60438, 62736, 71572, 71573, 71574, 71615, 75345 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 28749, "uuid": "014568b39a754538a27e25f24e7f1fca", "short_code": "coll", "title": "WCRP CMIP6: Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model output collection", "abstract": "World Climate Research Programme (WCRP) Coupled Model Intercomparison Project Phase 6 (CMIP6): Collection of simulations from the Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL model.\n\nThe official CMIP6 Citation, and its associated DOI, is provided as an online resource linked to this record." } ], "responsiblepartyinfo_set": [ 200806, 200807, 200808, 200809, 200810, 200811, 200812, 200814, 200815, 200813 ], "onlineresource_set": [ 85755, 85757, 85759, 85761, 85763, 85765, 85767, 85769, 85771, 85773, 85775, 85777, 85697, 85693, 85684, 85685, 85687, 85689, 85691, 85695, 85699, 85701, 85703, 85705, 85707, 85709, 85711, 85713, 85715, 85717, 85719, 85721, 85723, 85725, 85727, 85729, 85731, 85733, 85735, 85737, 85739, 85741, 85743, 85745, 85747, 85749, 85751, 85753, 85779, 85781, 85783, 85785, 85787, 85788, 85789 ] }, { "ob_id": 41262, "uuid": "2c1cb1d606c4421e9339a3028839a41f", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 10.1", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data (v10.1) was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the University of Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this data, please also cite the following peer-reviewed publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:21:53", "updateFrequency": "notPlanned", "dataLineage": "The FOCAL OCO-2 XCO2 retrieval development, data processing and analysis has received co-funding from ESA’s Climate Change Initiative (CCI+) via project GHG-CCI+ (contract 4000126450/19/I-NB, https://climate.esa.int/en/projects/ghgs) EUMETSAT via the FOCAL-CO2M study (contract EUM/CO/19/4600002372/RL), the European Union via the Horizon 2020 (H2020) projects VERIFY (Grant Agreement No. 776810, http://verify.lsce.ipsl.fr) and CHE (Grant Agreement No. 776186, https://www.che-project.eu) and by the State and the University of Bremen.\r\n\r\nData were supplied to CEDA for archiving as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, carbon dioxide, CO2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-20T16:47:59", "doiPublishedTime": "2024-03-01T10:06:50.290059", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41431, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_OC2_FOCA/v10.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 57708787190, "numberOfFiles": 2550, "fileFormat": "Data are in netCDF format." }, "timePeriod": { "ob_id": 11499, "startTime": "2014-09-06T00:00:00", "endTime": "2022-02-28T23:59:59" }, "resultQuality": { "ob_id": 4155, "explanation": "The dataset has been quality controlled, validated, and uncertainty characterized by the data provider (see related documents).", "passesTest": true, "resultTitle": "CCI GHG", "date": "2023-01-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33058, "uuid": "4d90fa654eda41b982c9b59de3b0fc4e", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm", "abstract": "The data has been generated from the OCO-2 satellite using the FOCAL-OCO2 algorithm" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50542, 50543, 55529, 55532, 55533, 55534, 55535, 63576, 69629, 74866, 74867, 74868, 74869, 74870, 74871, 74872, 74873, 74874, 74876, 74877, 75344 ], "vocabularyKeywords": [], "identifier_set": [ 12836 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 200821, 200822, 200823, 200824, 200825, 200826, 200827, 200828, 200829, 200830, 200831, 200832, 200833, 200834 ], "onlineresource_set": [ 85795, 85801, 85802, 85803, 85804, 85805, 85806, 85807, 85808 ] }, { "ob_id": 41264, "uuid": "ceaaf622b9eb45b7b1caa2529a7b93e2", "title": "Water Supply Stress Index model output German Landcover between 2000 - 2020.", "abstract": "The Water Supply Stress Index model was applied across Germany on a subbasin scale. The model output provides water yield (Q), evapotranspiration (ET), ecosystem productivity (GPP), carbon sequestration (NEP) and soil moisture datasets. The available period for this dataset ranges from 2000 - 2020. The model performs reasonably well against observed datasets (e.g. gauge station and eddy flux tower) and remotely sensed data (e.g. Modis).", "creationDate": "2024-01-11T11:27:47.559767", "lastUpdatedDate": "2024-01-11T11:46:12.055359", "latestDataUpdateTime": "2024-01-11T11:27:47.559779", "updateFrequency": "notPlanned", "dataLineage": "Data was produced using the WaSSI model, which was later validated against observed and remotely sensed data. The work is currently under consideration for a peer-reviewed journal publication.", "removedDataReason": "", "keywords": "Multi-timescale data,Landcover contribution,Carbon Sequestration Germany,Water Yield Germany", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4077, "bboxName": "", "eastBoundLongitude": 15.0, "westBoundLongitude": 5.0, "southBoundLatitude": 47.0, "northBoundLatitude": 55.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11458, "startTime": "2000-01-01T00:00:00", "endTime": "2020-12-31T00:00:00" }, "resultQuality": { "ob_id": 4477, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-11" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 200835, 200836, 200837, 200838, 200839, 200840, 200843, 200844, 200845, 200846, 200847, 200848, 200849, 200850, 200851 ], "onlineresource_set": [] }, { "ob_id": 41268, "uuid": "6285564c34a246fc9ba5ce053d85e5e7", "title": "ACSIS: Merged airborne chemistry data from instruments on board the FAAM aircraft", "abstract": "A collection of collated chemistry measurements made onboard the FAAM BAE-146 aircraft for The North Atlantic Climate System Integrated Study: ACSIS project. These data were collected by core and non-core instruments and compiled into a merged dataset. \r\n\r\nFlights included are from the measurement campaigns: ACSIS1 - February 2017, ACSIS2 - October 2017, ACSIS3 - May 2018, ACSIS4 - February 2019, ACSIS5 - August 2019, ACSIS6 - February 2020 and ACSIS7 - May 2022. Parameters include Carbon Dioxide (CO2), Methane (CH4), Nitric oxide (NO), Nitric dioxide (NO2), Total Organic Aerosol, Sulphate Aerosol, Nitrate Aerosol, Ammonium Aerosol, Chloride Aerosol, Urea (CH4N2O), Hydrogen cyanide (HCN), Nitryl chloride (ClNO2), and photolysis rates.", "creationDate": "2024-01-12T14:56:36.800281", "lastUpdatedDate": "2024-01-12T14:24:01", "latestDataUpdateTime": "2024-03-09T03:21:56", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "FAAM, chemistry", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-01-12T16:30:33", "doiPublishedTime": "2024-01-18T10:02:05", "removedDataTime": null, "geographicExtent": { "ob_id": 4078, "bboxName": "ACSIS FAAM flights", "eastBoundLongitude": 3.0, "westBoundLongitude": -38.1, "southBoundLatitude": 14.8, "northBoundLatitude": 53.4 }, "verticalExtent": null, "result_field": { "ob_id": 41266, "dataPath": "/badc/acsis/bae-146/merged-chemistry/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55447241, "numberOfFiles": 92, "fileFormat": "NASA-Ames, ICT" }, "timePeriod": { "ob_id": 11459, "startTime": "2017-02-13T00:00:00", "endTime": "2022-05-09T23:59:59" }, "resultQuality": { "ob_id": 4478, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41267, "uuid": "9e511b13e6af49f693adff84c7281fac", "short_code": "acq", "title": "ACSIS FAAM Merged Chemsitry", "abstract": "Core and non-core chemistry from onboard the FAAM BAE-146 aircraft collected for The North Atlantic Climate System Integrated Study: ACSIS project" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. 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They can also have critical impacts on sea-ice movement, sometimes resulting in ‘Very Rapid Ice Loss Events’ which present a major challenge to coupled forecasts of the Arctic environment from days out to a season ahead. As a result of global warming, sea ice is becoming thinner across large areas of the Arctic Ocean in summer. This means that winds can move it more easily and in turn the dynamic sea ice distribution is expected to feedback on the developing weather systems.\r\n\r\nThis project will fly two research aircraft into Arctic cyclones developing over the marginal ice zone at the edge of the Arctic Ocean basin. It will measure the turbulent exchange fluxes, flying low above the interface between atmosphere and ice, at the same time as measuring the wind and cloud structure of the cyclones above and the properties of the ice below. Combining the observations with numerical modelling experiments using the latest weather prediction models, the project aims to deduce the dominant physical processes acting and test theoretical mechanisms for two-way interaction between the Arctic cyclones and sea ice.\r\n\r\nThis project and the UK component of the field experiment are funded by the Natural Environment Research Council (NERC) NE/T006773/1, NE/T006811/1 and NE/T00682X/1. They will joined by teams from the USA and France, funded by the Office of Naval Research (USA)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61212, 61213, 61214, 61215, 61216, 61217, 61218, 61219, 61220, 61222, 61223, 61224, 61225, 61226, 61227, 61228, 61229, 61230, 61231, 61232, 61233, 74368, 74369 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 7571, "uuid": "8be3dd7cdf44090d89aeb8f105421506", "short_code": "coll", "title": "BAS Masin Twin-Otter aircraft data", "abstract": "Data is collected on board the British Antarctic Survey MASIN Twin Otter aircraft for a range of projects in the Antarctic and at other locations. These projects include atmospheric, boundary layer and cloud/aerosol studies.\r\n\r\n\r\nThe instrument suite includes standard temperature and water vapour sensors as well as a turbulence probe allowing full atmospheric profile measurements of temperature, dew point and winds. A DMT Cloud and aerosol spectrometer (CAPS) probe is used for cloud studies and a closed path Licor H2O/CO2 instrument, Grimm optical particle counter and cloud condensation nuclei counter are fed from simple Rosemount inlets." }, { "ob_id": 41339, "uuid": "eb195463b93c47a0aa52a59885908b75", "short_code": "coll", "title": "Arctic Summer-time Cyclones- Airborne meteorological measurements", "abstract": "Airborne in-situ observations of core meteorological data collected by the Meteorological Airborne Science INstrumentation (MASIN) instruments on board the British Antarctic Survey instrumented Twin Otter aircraft for the Arctic Summer-time Cyclones: Dynamics and Sea-ice Interaction project. The core data were collected in the Svalbard, Norway and Iceland Sea region between 29th July 2022 and 20th August 2022. The data is from a variety of instruments (temperature, humidity, wind, surface temperature, radiation and position), it is stored in NetCDF format using CF conventions.\r\n\r\nThe UK component of the Arctic Summertime-cyclones project field experiment are funded by the Natural Environment Research Council (NERC) NE/T006773/1, NE/T006811/1 and NE/T00682X/1. They will joined by teams from the USA and France, funded by the Office of Naval Research (USA)." } ], "responsiblepartyinfo_set": [ 201204, 201205, 201206, 201207, 201208, 201209, 201210, 201211, 201212, 201213, 201214, 201215, 201216, 201237, 201238 ], "onlineresource_set": [ 85849, 85864 ] }, { "ob_id": 41345, "uuid": "aeb9345956454f0b8d66bb6e078d55ec", "title": "Single Particle Soot Photometer (SP2) data from SASSO project - Version 0", "abstract": "This dataset contains number and mass concentration data of refractory black carbon (rBC) from the University of Manchester Single Particle Soot Photometer (SP2). Data were collected at the University of Exeter wildFIRE lab, between May and June 2019, and at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. These data were used to derive the refractive index of black carbon aerosols.\r\nThis version 0 dataset contains black carbon concentrations. The SP2 instrument (Droplet Measurement Technologies, Colorado, USA) is operated by University of Manchester scientists.", "creationDate": "2024-01-23T14:09:16.535483", "lastUpdatedDate": "2024-01-23T14:10:12", "latestDataUpdateTime": "2024-01-23T14:09:16", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the University of Manchester instrument scientists before preparation for archiving and delivery to the BADC.", "removedDataReason": "", "keywords": "SP2, Black carbon, SASSO, Single Particle Soot Photometer, man-sp2", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-07-31T09:37:40", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41627, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_SP2_LABdata", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10066392, "numberOfFiles": 11, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11491, "startTime": "2019-05-13T00:00:00", "endTime": "2020-02-28T00:00:00" }, "resultQuality": { "ob_id": 4498, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41346, "uuid": "5a52d997cf3d4078a2c328fe6bca74bd", "short_code": "acq", "title": "Acquisition for: Single Particle Soot Photometer (SP2) data from SASSO project- Version 0", "abstract": "Acquisition for: Single Particle Soot Photometer (SP2) data from SASSO project- Version 0" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. 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Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 84623, 84624, 84625, 84626 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201284, 201285, 201286, 201287, 201288, 201289, 201291, 202886 ], "onlineresource_set": [ 86602, 86603 ] }, { "ob_id": 41347, "uuid": "8cd84a935fdf4e28b8f42b95ebeee657", "title": "EXSCALABAR optical data from SASSO project - Version 0", "abstract": "This dataset contains data from the University of Manchester EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) collected at the University of Exeter, between May and June 2019, and at the University of Manchester, between January 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. EXSCALABAR is a custom-built aerosol optical spectroscopy instrument. In this study, it was used to measure the absorption and extinction coefficients of particles at 405 nm and 660 nm. These data were then used to derive the refractive index of the aerosols.\r\nThis version 0 dataset contains light absorption/extinction coefficient at multiple wavelengths. The EXSCALABAR is operated by Met office scientists.", "creationDate": "2024-01-23T14:25:26.902805", "lastUpdatedDate": "2024-01-23T14:49:11", "latestDataUpdateTime": "2024-08-02T01:46:21", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the Met office instrument scientist before preparation for archiving and delivery to the BADC.", "removedDataReason": "", "keywords": "EXSCALABAR,aerosol absorption coefficient,aerosol extinction coefficient", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-08-01T15:51:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41624, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_EXSCALABAR_LABdata/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11300447, "numberOfFiles": 13, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11493, "startTime": "2019-05-13T00:00:00", "endTime": "2020-01-24T00:00:00" }, "resultQuality": { "ob_id": 4499, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41348, "uuid": "74e1d6e39d2247cda2c71a496e7a9ce5", "short_code": "acq", "title": "Acquisition for: EXSCALABAR optical data from SASSO project- Version 0", "abstract": "" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. 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Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201293, 201294, 201295, 201296, 201297, 201298, 201299, 202884, 201300 ], "onlineresource_set": [ 86606, 86607 ] }, { "ob_id": 41350, "uuid": "180b9636108a46c99611a288f60bb894", "title": "Aerosol Mass Spectrometer (AMS) data from SASSO project- Version 0", "abstract": "This dataset contains real-time measurements organic aerosol mass concentration data from the University of Manchester Aerosol Mass Spectrometer (AMS). These data were collected at the University of Exeter, between May and June 2019 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. Combined with the black carbon mass concentration measured by the Single Particle Soot Photometer (SP2), this information can be used to identify the burning phase (pyrolysis, flaming, or smouldering) in wood combustion experiments.\r\nThis version 0 dataset contains organic mass concentration. The AMS is operated by University of Manchester scientists.", "creationDate": "2024-01-23T15:16:43.704298", "lastUpdatedDate": "2024-01-23T15:16:43", "latestDataUpdateTime": "2024-01-23T15:16:43", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the University of Manchester and supplied for archiving at the Centre for Environmental Data Analysis (CEDA)", "removedDataReason": "", "keywords": "AMS, Organic mass concentration, man-ams", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-07-30T09:57:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41629, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_AMS_LABdata", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9767526, "numberOfFiles": 10, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11494, "startTime": "2019-05-13T00:00:00", "endTime": "2020-02-28T00:00:00" }, "resultQuality": { "ob_id": 4500, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41351, "uuid": "1da823bd305042feaf19a3e46a2b556c", "short_code": "acq", "title": "Acquisition for: Aerosol Mass Spectrometer (AMS) data from SASSO project- Version 0", "abstract": "Acquisition for: Aerosol Mass Spectrometer (AMS) data from SASSO project- Version 0" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. 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Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201303, 201304, 201305, 201306, 201307, 201308, 204552, 201310 ], "onlineresource_set": [ 86604, 86605 ] }, { "ob_id": 41352, "uuid": "3fa0152b55fd4a3b8357fdfcfc55f45d", "title": "NOx and O3 gas data from SASSO project - Version 0", "abstract": "This dataset contains data from the University of Manchester nitrogen oxides (NOx) monitor and ozone (O3) monitor collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. The NOx and O3 concentrations inside the chamber were measured in real time during the experiment. \r\n\r\nNO, NO2, and NOx concentrations were measured using the NOx analyzer (Model 42i, Thermo Scientific), while O3 concentration was measured using the O3 analyzer (Model 49C, Thermo Scientific). These data reveal the conditions for each experiment.\r\n\r\nThis version 0 dataset contains NO, NO2, NOx and O3 concentration. The NOx monitor and O3 monitor are operated by University of Manchester scientists.", "creationDate": "2024-01-23T15:27:08.337757", "lastUpdatedDate": "2024-01-23T15:27:35", "latestDataUpdateTime": "2024-08-02T01:46:22", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the SASSO project and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "NOX, O3, SASSO", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-08-01T15:57:38", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41625, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_GAS_LABdata", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 440773, "numberOfFiles": 5, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11496, "startTime": "2020-01-10T00:00:00", "endTime": "2020-01-24T00:00:00" }, "resultQuality": { "ob_id": 4501, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 43067, "uuid": "f03b4da197424e72aa992e868d7289a7", "short_code": "acq", "title": "Acquisition for: NO, NO2, and NOx concentration data measured by the from Thermo Scientific 42i NOx analyser instrument and O3 concentration data is measured by the O3 analyser instrument (Model 49C, Thermo Scientific) at the University of Manchester, between January and February 2020.", "abstract": "Acquisition for: NO, NO2, and NOx concentration data measured by the from Thermo Scientific 42i NOx analyser instrument and O3 concentration data is measured by the O3 analyser instrument (Model 49C, Thermo Scientific) at the University of Manchester, between January and February 2020." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. 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Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201312, 201313, 201314, 201315, 201316, 201317, 202885, 201319 ], "onlineresource_set": [ 86608, 86609 ] }, { "ob_id": 41353, "uuid": "a93252bc9a0a489c978d77b2b365e2e7", "title": "Scanning Mobility Particle Sizer (SMPS) data from SASSO project - Version 0", "abstract": "This dataset contains number size distribution of monodisperse secondary organic aerosols (SOA) data from the University of Manchester Scanning Mobility Particle Sizer (SMPS) collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. These data were used to derive the refractive index of the SOA.\r\nThis version 0 dataset contains particle size distribution. The SMPS is operated by University of Manchester scientists.", "creationDate": "2024-01-23T15:35:54.540590", "lastUpdatedDate": "2024-01-23T15:35:54", "latestDataUpdateTime": "2024-08-02T01:46:34", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the SASSO project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "SMPS, Size distribution, SASSO", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-07-31T12:34:56", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41628, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_SMPS_LABdata", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5418, "numberOfFiles": 3, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11497, "startTime": "2020-01-06T00:00:00", "endTime": "2020-02-28T00:00:00" }, "resultQuality": { "ob_id": 4502, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41354, "uuid": "d0836143b9034dcdbd8a5250bafb8c5f", "short_code": "acq", "title": "Acquisition for: Scanning Mobility Particle Sizer (SMPS) data from SASSO project- Version 0", "abstract": "Acquisition for: Scanning Mobility Particle Sizer (SMPS) data from SASSO project- Version 0" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. Although BC and BrC are very important for climate, they are poorly represented in atmospheric models. This is in part due to the complex microphysical properties of BC and the lack of accurate refractive index (RI) descriptions for both BC and BrC.\r\nSASSO capitalized on a unique combination of new technical and methodological developments at the University of Manchester and the University of Exeter to generate experimental data on a variety of soot types, providing much greater constraints on optical properties than previously possible. Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201320, 201321, 201322, 201323, 201324, 201325, 201326, 202887, 201327 ], "onlineresource_set": [ 86610, 86611 ] }, { "ob_id": 41356, "uuid": "25e39573126840d5bbb843902afc729b", "title": "Differential Mobility Particle Sizer (DMPS) data from SASSO project - Version 0", "abstract": "This dataset contains number size distribution data from the University of Manchester Differential Mobility Particle Sizer (DMPS). The custom-built DMPS measured the evolution of size distribution for polydisperse particles formed from the ozonolysis of α-pinene (13/01/2020) or cresol (10/01/2020) in the Manchester aerosol chamber. Additionally, similar measurements were conducted under the same conditions but with black carbon (BC) as the seed (23/01/2020: BC + α-pinene; 22/01/2020: BC + cresol). These data were collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. This information can reveal the evolution of the particles inside the chamber.\r\n\r\nThis version 0 dataset contains particle size distribution. The DMPS is operated by University of Manchester scientists.", "creationDate": "2024-01-23T15:41:34.587905", "lastUpdatedDate": "2024-01-23T15:41:34", "latestDataUpdateTime": "2024-09-11T13:15:57", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the SASSO project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "DMPS, Particle size distribution, SASSO", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-09-04T14:08:17", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 43070, "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_DMPS_LABdata", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 91925, "numberOfFiles": 5, "fileFormat": "Data are BADC-CSV formatted" }, "timePeriod": { "ob_id": 11498, "startTime": "2020-01-06T00:00:00", "endTime": "2020-02-28T00:00:00" }, "resultQuality": { "ob_id": 4503, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-23" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41357, "uuid": "1d07ca64c6d9465b9a269531a0d89900", "short_code": "acq", "title": "Acquisition for: Differential Mobility Particle Sizer (DMPS) data from SASSO project- Version 0", "abstract": "" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38101, "uuid": "7a4feaf0fd624dacaf34a0129f074fcc", "short_code": "proj", "title": "Soot Aerodynamic Size Selection for Optical properties (SASSO)", "abstract": "Black carbon (BC) and brown carbon (BrC) aerosols are widely studied components of atmospheric aerosol because they can absorb solar radiation and heat the atmosphere, causing positive radiative forcing of the climate. Although BC and BrC are very important for climate, they are poorly represented in atmospheric models. This is in part due to the complex microphysical properties of BC and the lack of accurate refractive index (RI) descriptions for both BC and BrC.\r\nSASSO capitalized on a unique combination of new technical and methodological developments at the University of Manchester and the University of Exeter to generate experimental data on a variety of soot types, providing much greater constraints on optical properties than previously possible. Specifically, SASSO used the Aerodynamic Aerosol Classifier (AAC) for size selection of emissions from wood burning, diesel combustion, and secondary organic aerosol (SOA) formation, prior to optical measurements using cavity ring-down and photoacoustic spectroscopy with the EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) instrumentation, custom-built by the Met Office.\r\nIn addition, to test the \"lensing effect,\" non-absorbing and weakly absorbing organic materials were condensed onto BC to form coated soot particles. The mass concentration of black carbon aerosol was measured using the Single Particle Soot Photometer (SP2), and chemical information was obtained via Aerosol Mass Spectrometry (AMS), allowing different optical models for mixed particles to be evaluated." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201330, 201331, 201332, 201333, 201334, 201335, 201337, 204556 ], "onlineresource_set": [ 86612, 86613 ] }, { "ob_id": 41358, "uuid": "8175ede3a1d642deba8f4cce49d7bda8", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.8, November 2017 - October 2023", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD. This version of the product is version 1.8, and covers the period from November 2017 - October 2023. \r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThese data were produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.\r\n\r\nWhen citing this dataset, please also cite the following peer-reviewed publication: \r\nSchneising, O., Buchwitz, M., Hachmeister, J., Vanselow, S., Reuter, M., Buschmann, M., Bovensmann, H., and Burrows, J. P.: Advances in retrieving XCH4 and XCO from Sentinel-5 Precursor: improvements in the scientific TROPOMI/WFMD algorithm, Atmos. Meas. Tech., 16, 669–694, https://doi.org/10.5194/amt-16-669-2023, 2023.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2024-02-06T17:32:14", "latestDataUpdateTime": "2024-03-09T03:22:17", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA Climate Change Initiative (CCI) Greenhouse Gas project team and supplied to CEDA as part of the ESA CCI Open Data Portal.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, Sentinel-5 Precursor, TROPOMI, atmosphere, methane, carbon monoxide", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-19T11:30:56", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41449, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_S5P_WFMD/v1.8", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 126935347162, "numberOfFiles": 2136, "fileFormat": "Data are in netCDF format." }, "timePeriod": { "ob_id": 11555, "startTime": "2017-11-13T00:00:00", "endTime": "2023-10-31T23:59:59" }, "resultQuality": { "ob_id": 3389, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41447, "uuid": "a58731401860459bae57fb1cc4b183d4", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD v1.8 product", "abstract": "The ESA Greenhouse Gases Climate Change Initiative CH4_S5P_WFMD v1.8 product has been derived from the TROPOMI instrument on the Sentinel-5P satellite, using the WFM-DOAS retrieval algorithm." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54763, 54764, 55532, 55533, 63576, 66578, 66973, 66974, 68636, 68637, 69622, 69623, 69628, 69634, 69635, 74642, 74643, 74644, 74645, 74646, 74647, 74648, 74649, 74650, 74651, 74652, 74653, 74654, 74655, 74656, 74657, 74658, 74659, 74660 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 201339, 201346, 201340, 201341, 201342, 201343, 201344, 201345, 201348, 201347, 201349, 201350, 201351 ], "onlineresource_set": [ 85878, 85879, 85866, 85873, 85874, 85875, 85876, 86037 ] }, { "ob_id": 41379, "uuid": "bd67917f85d54d80927ff0134120ccd9", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Cover Fraction Viewable (SCFV) composite from AVHRR data of all platforms (1982 – 2022)", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFV product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFV product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\n\r\nThe final product is quality checked.", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "underDevelopment", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11205, "startTime": "1979-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3843, "explanation": "The unbiased root mean square error per-pixel is added as an uncertainty layer in the product. The AVHRR based SCFV product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality, see the Snow_cci documentation.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-01-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33470, "uuid": "64b26970ed084e8bbdbd38d4e948c7cc", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFV AVHRR v2.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201387, 201388, 201389, 201390, 201391, 201392, 201393, 201394, 201395, 201396, 201397, 201398, 201399, 201400, 201401 ], "onlineresource_set": [ 85885, 85886, 85887, 85888, 85889, 85890, 85891, 85892, 85893, 85894, 85895, 85896 ] }, { "ob_id": 41380, "uuid": "c32dae91c2884e0681d34e91c1bb9ad6", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Cover Fraction on Ground (SCFG) composite from AVHRR data of all platforms (1982 – 2022)", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1982-2018. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the Cloud CCI cloud v3.0 mask product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern is responsible for the SCFG product development and generation. ENVEO developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises one longer data gap of 92 between November 1994 and January 1995, and 16 individual daily gaps, resulting in a 99% data coverage over the entire study period of 37 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "The snow_cci SCFG product based on AVHRR was developed and processed at the University of Bern in the frame of ESA CCI+ Snow project. The AVHRR baseline FCDR was pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project (Devasthale et al. 2017, Stengel et al. 2020). \r\nThe final product is quality checked.\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Snow, Snow Cover Fraction", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "underDevelopment", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11204, "startTime": "1979-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 3893, "explanation": "The unbiased root mean square error per-pixel is added as uncertainty layer in the product. The AVHRR based SCFG product matches the CCI data standards version 2.3, released in July 2021. For more information on data quality see the Snow_cci documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 33471, "uuid": "552e5a4245d24972902cb667ac881dca", "short_code": "cmppr", "title": "Composite process for the ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product", "abstract": "The ESA Snow Climate Change Initiative SCFG AVHRR v2.0 product is based on an AVHRR baseline FCDR pre-processed using pyGAC and pySTAT in the frame of the ESA CCI Cloud project.\r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 630 nm and 1.61 µm (channel 3a or the reflective part of channel 3b), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground." }, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201402, 201403, 201404, 201405, 201406, 201407, 201408, 201409, 201410, 201411, 201412, 201413, 201414, 201415, 201416 ], "onlineresource_set": [ 85897, 85908, 85898, 85905, 85899, 85900, 85906, 85901, 85902, 85903, 85907, 85904 ] }, { "ob_id": 41381, "uuid": "0c37b06d024d4f8eb6f7ffc0c66d3b4e", "title": "Wind speed and direction data from Gill sonic windmaster instrument at London Honor Oak Park Air Quality Supersite", "abstract": "One minute wind speed and wind direction data at 16m above ground level from a Gill Sonic Windmaster instruments at the London Honor Oak Park Air Quality Supersite (LHOP) for the Integrated Research Observation System for Clean Air (OSCA) project, 2021 onwards.", "creationDate": "2024-01-26T12:15:40.760193", "lastUpdatedDate": "2024-01-26T12:39:01", "latestDataUpdateTime": "2024-03-09T03:22:23", "updateFrequency": "notPlanned", "dataLineage": "Data is saved directly from the instrument to the computer, before being transferred to the general database, where it is processed, automatically averaged over minute time intervals and flagged where data is suspect. Once complete, data is uploaded to CEDA for archiving.", "removedDataReason": "", "keywords": "HOP,OSCA,London,wind", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-02-12T10:15:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4123, "bboxName": "", "eastBoundLongitude": -0.037418, "westBoundLongitude": -0.037418, "southBoundLatitude": 51.449674, "northBoundLatitude": 51.449674 }, "verticalExtent": null, "result_field": { "ob_id": 41461, "dataPath": "/badc/osca/data/london/OSCA_LHOP_Sonic_Windmaster/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 36999214, "numberOfFiles": 35, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11513, "startTime": "2021-03-15T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4505, "explanation": "Wind speed data that reads above 100 and below 0 m/s are flagged as suspect and are excluded, wind direction that reads below 0 and above 360 degrees is flagged as suspect and excluded", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-26" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41386, "uuid": "cb199ce0e0244f449514ab95b28ab039", "short_code": "acq", "title": "Acquisition for: Wind speed and direction data from Gill sonic windmaster instrument at London Honor Oak Park", "abstract": "Wind speed and direction data from Gill sonic windmaster instrument at London Honor Oak Park for the OSCA project" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 37963, "uuid": "ab605b618884401c91afd0274c92144e", "short_code": "proj", "title": "Integrated Research Observation System for Clean Air (OSCA)", "abstract": "The Integrated Research Observation System for Clean Air (OSCA) is a multidisciplinary research project, combining atmospheric observations, laboratory studies, data processing development and integrated scientific synthesis to deliver improved understanding of urban air pollution in the UK, and enable delivery of key objectives of the Clean Air: Analysis and Solutions Programme. OSCA exploits recent significant UK investment in air pollution measurement infrastructure - the air pollution supersites in London, Birmingham and Manchester - and other new UKRI-funded capability developed for field, modelling and laboratory studies of air pollution processes. \r\n\r\nThe project brings together a research team spanning 5 UK HEIs and 2 NERC centres across disciplines of atmospheric science, engineering, mathematics, chemistry, physics and computer science and includes investigators with direct involvement in the provision of science advice in support of policy development - through established links with (e.g.) Department for Environment, Food and Rural Affairs (Defra), Department for Transport (DfT), Department of Health (DoH), regional policymakers, and international bodies. OSCA will provide scientific insights that will inform implementation of the new UK Clean Air Strategy, contribute to development and evaluation of regional air quality policy measures, and enable the development and optimisation of emission abatement measures, for the protection of human health. The project provides a definitive assessment of the current state of UK urban air quality, and of trends in air pollutants - both those expected in response to policy and changing behaviour, and unanticipated consequences of these - and provides data and infrastructure to underpin other proposed projects in the Clean Air Programme. NE/T001984/1, \tNE/T001917/1, NE/T001909/2, NE/T001798/1, NE/T001925/1, NE/T001798/2, NE/T001976/1, NE/T001909/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61969, 75316, 75317, 75318, 75319 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 37907, "uuid": "65b50d3348cb4745bb7acfcf6f2057b8", "short_code": "coll", "title": "Integrated Research Observation System for Clean Air (OCSA): Birmingham, Manchester and London air quality supersites data collection", "abstract": "A collection of ground-based observation datasets made at 3 air quality supersites in Manchester, Birmingham and London for the Integrated Research Observation System for Clean Air (OSCA) project which is part of the Strategic Priorities Fund (SPF) Clean Air Programme. \r\n\r\nMeasurements were made of a broad range of atmospheric components by similar comprehensive instrument suites at the 3 sites on a longterm basis beginning at the end of 2018. Additional instrumentation was temporarily added for intensive operation periods in June 2021 and January 2022.\r\n\r\nThis project was a collaboration between University of Manchester, University of Birmingham, Kings College London, University of Cambridge, University of York, Centre for Ecology and Hydrology, National Centre for Atmospheric Science (NCAS) and University of Edinburgh." }, { "ob_id": 45670, "uuid": "824c0c058de442ab885431eba938dc03", "short_code": "coll", "title": "NCAS archive of Air quality measurements from Manchester, Birmingham and London air quality supersites", "abstract": "This collection contains maeasurements of air quality parameters from a suite of instruments deployed at 3 urban supesites located in Manchester, Birmingham and London Honor Oak Park.\r\n\r\nMore details to follow." } ], "responsiblepartyinfo_set": [ 201430, 201417, 201418, 201419, 201420, 201421, 201422, 202118, 201429 ], "onlineresource_set": [ 86063 ] }, { "ob_id": 41388, "uuid": "b82b58d085d0433b821f4ae31cb608de", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2023, v3.4.0.2023f", "abstract": "This is version v3.4.0.2023f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data.\r\n\r\nThis update (v3.4.0.2023f) to HadISD corrects a long-standing bug which was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20240101_v3.4.1.2023f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-03-21T01:53:39", "updateFrequency": "notPlanned", "dataLineage": "HadISD the global sub-daily station dataset was produced by the Met Office Hadley Centre. It was derived from the Integrated Surface Dataset (ISD) from NOAA's National Climatic Data Center (NCDC). HadISD has been passed to the Centre for Environmental Data Analysis (CEDA) for archiving and distribution.", "removedDataReason": "", "keywords": "HadISD, temperature, dewpoint, pressure, wind, speed, direction, cloud, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2024-02-23T09:22:32", "doiPublishedTime": "2024-02-23T14:10:49", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41389, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISD/subdaily/HadISDTable/r1/v3-4-0-2023f/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 55510914661, "numberOfFiles": 9687, "fileFormat": "Data are NetCDF formatted." }, "timePeriod": { "ob_id": 11514, "startTime": "1931-01-01T00:00:00", "endTime": "2023-12-31T23:00:00" }, "resultQuality": { "ob_id": 3104, "explanation": "CF-Compliant NetCDF, see documentation for quality control processes used to produce these data. These data are quality controlled by the data provider, the Met Office Hadley Centre (MOHC) and not the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-02-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20043, "uuid": "32eff53af32442d1a347da2cc45bb9db", "short_code": "comp", "title": "HadISD station data processing performed at the Met Office Hadley Centre", "abstract": "The HadISD station data were produced by the Met Office Hadley Centre. Individual station data within the ISD were selected selected on the basis of their length of record and reporting frequency. A merging algorithm using their location, elevation and station name identified candidates suitable to combine together. All stations were passed through a suite of automated quality control tests designed to remove bad data whilst keeping the extremes. None of the ISD flags were used in this process. The QC tests focussed on the temperature, dewpoint temperature and sea-level pressure variables, although some were applied to the wind speed and direction and cloud data. The data files also contain other variables which were pulled through from the raw ISD record, but have had no QC applied (e.g. cloud base and precipitation depth). \r\n\r\nNotes:\r\n1. These data have not yet been homogenised and so trend fitting should be undertaken with caution. The homogeneity has been assessed and results are available from the Met Office Hadley Centre HadISD website: http://www.metoffice.gov.uk/hadobs/hadisd/. \r\n2. A long-standing bug (affecting versions v2.0.2_2017p through to v3.3.0.2022f), was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. This was corrected for the later version v3.4.0.2023f to HadISD. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html(v2.0.2_2017p through to v3.3.0.2022f), and as noted this has been fixed for v3.4.02023f.\r\n\r\n\r\nFor further details see: \r\nDunn, R. J. H., et al., (2016), Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, and Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations\r\nfrom 1973-2011, Climate of the Past.\r\nDunn, R. J. H., et al. (2014), Pairwise Homogeneity Assessment of HadISD, Climate of the Past, 10, 1501-1522 (see Docs for links to publications)." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2561, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 32, "licenceURL": "http://www.nationalarchives.gov.uk/doc/non-commercial-government-licence/version/2/", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 13164, "uuid": "ce252c81a7bd4717834055e31716b265", "short_code": "proj", "title": "Met Office Hadley Centre - Observations and Climate", "abstract": "The Met Office Hadley Centre is one of the UK's foremost climate change research centres.\r\n\r\nThe Hadley Centre produces world-class guidance on the science of climate change and provide a focus in the UK for the scientific issues associated with climate science.\r\n\r\nLargely co-funded by Department of Energy and Climate Change (DECC) and Defra (the Department for Environment, Food and Rural Affairs), the centre provides in-depth information to, and advise, the Government on climate science issues.\r\n\r\nAs one of the world's leading centres for climate science research, the Hadley Centre scientists make significant contributions to peer-reviewed literature and to a variety of climate science reports, including the Assessment Report of the IPCC. The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54776, 54777, 54778, 54780, 54781, 54782, 54783, 54784, 54785, 54786, 54787, 54788, 54789, 54790, 54791, 54792, 54793, 54794, 54795, 62629, 62630, 62631, 62632, 62633, 62634, 62635, 62636, 62637, 62638 ], "vocabularyKeywords": [], "identifier_set": [ 12830 ], "observationcollection_set": [ { "ob_id": 13521, "uuid": "f579035b3c954475922e4b13705a7669", "short_code": "coll", "title": "HadISD: global sub-daily station data for climate extremes", "abstract": "HadISD is a station based dataset comprising 6103 stations covering 1973-present. These stations are a subset of the stations available in the Integrated Surface Database (ISD), and are ones selected to be those most useful for climate studies (long records and high reporting frequency). Individual stations within the ISD were composited when it was appropriate to do so to improve the coverage.\r\n \r\nHadISD is a multi-variate dataset, where the following fields are available: temperature, dewpoint temperature, sea-level pressure, wind speed, wind direction and cloud data (total, low, mid and high levels). These variables are all quality controlled using an automatic suite of tests, the code for which is available on request. The QC tests were designed to remove bad data whilst keeping true extremes. A number of other variables are also carried through to the final NetCDF files, but have not been quality controlled (e.g. precipitation period, precipitation depth, sunshine duration)." } ], "responsiblepartyinfo_set": [ 201439, 201434, 201435, 201436, 201437, 201438, 201440, 201441, 201442, 201445, 201444, 201443 ], "onlineresource_set": [ 85917, 85914, 85911, 85909, 85910, 85912, 85913, 85916, 85915 ] }, { "ob_id": 41396, "uuid": "2bfbdba03d9b423f99cadf404ca2daab", "title": "HadEX3: global land-surface climate extremes indices v3.0.4 (1901-2018); ETSCI extension", "abstract": "HadEX3-ETSCI is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. The indices in this extension to HadEX3 are those which are recommended by the World Meteorological Organization (WMO) Expert Team on Sector-specific Climate Indices (ET-SCI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). There are minor changes in the input data and also quality control checks which have been included in this dataset which are different to those in HadEX3, version 3.0.4, which contains parameters as defined by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI).\r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nIndices are available as annual and/or monthly quantities. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nTo align this extension with the existing ETCCDI indices in HadEX3, we commence versioning at 3.0.4.", "creationDate": "2024-01-26T16:19:34.488142", "lastUpdatedDate": "2024-01-26T16:19:34", "latestDataUpdateTime": "2025-01-10T01:56:08", "updateFrequency": "notPlanned", "dataLineage": "HadEX3 is a dataset of gridded land-surface temperature and precipitation extremes indices and was produced by the Met Office Hadley Centre in collaboration with the ARC Centre of Excellence for Climate Extremes at the University of New South Wales and many data contributors from institutes and organisations around the world. The extremes indices were developed by the former WMO Expert Teams on Climate Change Detection and Indices (ETCCDI), Climate Risk and Sector-specific (ET-CRSCI) and Sector-specific Climate Indices (ET-SCI), and derived from daily, station based observations. These have undergone quality control checks and then been blended into a gridded product using an angular distance weighting routine.", "removedDataReason": "", "keywords": "HadEX3, indices, temperature, precipitation, extremes, monthly, annual, land, surface, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": "2024-03-05T09:38:12", "doiPublishedTime": "2024-03-05T09:37:37.859069", "removedDataTime": null, "geographicExtent": { "ob_id": 4125, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41397, "dataPath": "/badc/ukmo-hadobs/data/derived/MOHC/HadOBS/HadEX3/v3-0-4-etsci/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4550192669, "numberOfFiles": 93, "fileFormat": "Data are NetCDF formatted" }, "timePeriod": { "ob_id": 11517, "startTime": "1901-01-01T00:00:00", "endTime": "2018-12-31T00:00:00" }, "resultQuality": { "ob_id": 4507, "explanation": "The extremes indices have undergone quality control checks at the station level to ensure consistency. 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Since sea ice is a sensitive climate indicator with large seasonal and regional variability, the climate research community require long-term and regular observations of the key ice parameters in both Arctic and Antarctic." } ], "responsiblepartyinfo_set": [ 201695, 201700, 201782, 201783, 201784, 201785, 201786, 201787, 201702, 201704 ], "onlineresource_set": [ 85996, 85993, 85994, 86061, 86062 ] }, { "ob_id": 41413, "uuid": "e74491c96ef24df29a9342a3d57b5939", "title": "ACSIS: Sunphotometer aerosol measurements at Plymouth Marine Laboratory - Version 1. 2001-2023", "abstract": "Measurements of spectral aerosol optical thickness (tau), single scattering albedo (SSA), refractive index (RI), and size distribution of aerosols for preset aerosol diameters (um - microns) made using sunphotometers between June 2001 - December 2023 located on the roof of Plymouth Marine Laboratory (PML), Devon, UK, for the Western Channel Observatory (https://www.westernchannelobservatory.org.uk) and The North Atlantic Climate System Integrated Study (ACSIS). \r\n\r\nThis version 1 dataset contains measurements for 2001 to 2023. Two Kipp and Zonen (UK) PREDE POM01-L sunphotometers were used: June 2001 - March 2008 (Unknown serial number) and August 2009 - December 2023 (Serial Number PS08015). \r\n\r\nThe instruments operated at 7 wavelengths (315, 400, 500, 670, 870, 940 and 1020nm) and scanned at pre-set angles away from the solar disk to determine, using the inversion code of Nakajima et al. (1996), various optical properties of tiny particles called aerosols within the atmospheric column.", "creationDate": "2024-01-29T16:37:33.813661", "lastUpdatedDate": "2024-01-29T16:10:31", "latestDataUpdateTime": "2024-03-09T03:22:26", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ACSIS, aerosol, PML, photometer", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-01-30T08:03:21", "doiPublishedTime": "2024-01-31T14:19:35.067207", "removedDataTime": null, "geographicExtent": { "ob_id": 4126, "bboxName": "PML", "eastBoundLongitude": -4.1477, "westBoundLongitude": -4.1477, "southBoundLatitude": 50.3659, "northBoundLatitude": 50.3659 }, "verticalExtent": null, "result_field": { "ob_id": 41411, "dataPath": "/badc/acsis/pml-sunphotometer/data/v1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 63129401, "numberOfFiles": 4, "fileFormat": "BADC-CSV, txt" }, "timePeriod": { "ob_id": 11526, "startTime": "2001-05-22T00:00:00", "endTime": "2023-12-31T12:00:00" }, "resultQuality": { "ob_id": 4508, "explanation": "Research data from the ACSIS project", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-29" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41412, "uuid": "cb5533c995684bb99bf34bbc4b8be772", "short_code": "acq", "title": "Aquisition for PML-sunphotometer for ACSIS", "abstract": "Measurements of spectral aerosol optical thickness (tau), single scattering albedo (SSA), refractive index (RI), and size distribution of aerosols for preset aerosol diameters (um - microns) made using Kipp and Zonen (UK) PREDE POM01-L sunphotometers between June 2001 - December 2023 for the Western Channel Observatory (https://www.westernchannelobservatory.org.uk) and The North Atlantic Climate System Integrated Study (ACSIS)." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12807 ], "observationcollection_set": [ { "ob_id": 24737, "uuid": "31ae96f9cfc54ef9a38638f8723a1d17", "short_code": "coll", "title": "ACSIS: Data collected during the The North Atlantic Climate System Integrated Study.", "abstract": "This data collection includes a range of data collected for The North Atlantic Climate System Integrated Study: ACSIS, including: In-situ airborne observations by the FAAM BAE-146 aircraft, groundbased air composition measurements from Penlee Observatory, and Atlantic Ocean Sea Surface Temperature (SST) studies." } ], "responsiblepartyinfo_set": [ 201710, 201711, 201712, 201713, 201714, 201715, 201716, 201726 ], "onlineresource_set": [ 85999, 86000, 86001 ] }, { "ob_id": 41414, "uuid": "88b969c2540e4d558c2896fb5b8ef613", "title": "ACSIS: UKESM1 simulations for the North Atlantic Climate System Integrated Study", "abstract": "Three UK Earth Sysyem Model (UKESM1) hindcasts have been performed in support of the North Atlantic Climate System Integrated Study (ACSIS). \r\n\r\nData is provided as raw model output in Met Office PP (32-bit) format that can be read by the Iris (https://scitools-iris.readthedocs.io/en/stable/) or cf-python (https://ncas-cms.github.io/cf-python/) libraries.\r\n\r\nThis is global data at N96eL85 resolution (1.875 x 1.25, 85 model levels up to 85km). Simulations were performed on the Monsoon2 High Performance Computer (HPC).\r\n\r\nThe following fields are contained in the dataset: O3, NO, NO2, CO, CH4, 4x Stratospheric O3 tracers, and 30x idealised tracers emitted from various locations (15 with a 5-day e-folding lifetime, and 15 with a 30-day e-folding lifetime). Data is provided in mass mixing ratio (kg species/kg air).", "creationDate": "2024-01-29T16:50:17.061436", "lastUpdatedDate": "2024-01-29T18:47:49", "latestDataUpdateTime": "2024-03-09T03:22:30", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team using the UKESM1-StratTrop model deployed at NCAS-CAMBRIDGE and the output archived at CEDA", "removedDataReason": "", "keywords": "ACSIS, UKESM, ozone, nitric oxide, nitrogen dioxide, carbon monixide, methane", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-21T09:40:19", "doiPublishedTime": "2024-02-21T14:30:26.192336", "removedDataTime": null, "geographicExtent": { "ob_id": 4129, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41462, "dataPath": "/badc/acsis/UKESM1-hindcasts/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 503902032605, "numberOfFiles": 1376, "fileFormat": "pp format" }, "timePeriod": { "ob_id": 11536, "startTime": "1981-01-01T00:00:00", "endTime": "2020-02-29T00:00:00" }, "resultQuality": { "ob_id": 4511, "explanation": "These simulations have been developed over a number of years and had code reviews performed to ensure the coding standard used. All code developments have since been committed to the UM and UKCA trunks for use by the community.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 39348, "uuid": "15731dd5e693496480a0874d2da60398", "short_code": "comp", "title": "UKESM1-StratTrop model deployed at NCAS-CAMBRIDGE", "abstract": "One simulation is a UKESM1.0 configuration at UM version 11.5 with additional diagnostics added and \"nudged\" to ERA-Interim reanalyses for 1981-2014 (u-bv711 and u-bw316) and continued using SSP3-7.0 forcings to 2019 (u-by117).\r\n\r\nOne simulation is a UKESM1.0 configuration at UM version 11.5 with additional diagnostics added and \"nudged\" to ERA-5 reanalyses for 1982-2014 (u-bw784) and continued using SSP3-7.0 forcings to 2020 (u-by803). The bugfix documented in Ranjithkumar et al. 2021 has been applied (DOI:10.5194/acp-21-4979-2021).\r\n\r\nOne simulation is a UKESM-StratTrop configuration with stratospheric ozone improvements at UM version 11.5 with additional diagnostics and \"nudged\" to ERA-5 reanalyses for 1982-2014 (u-bv828 and u-bx320) and continued using SSP3-7.0 forcings to 2020 (u-by808). The bugfix documented in Ranjithkumar et al. 2021 has been applied (DOI:10.5194/acp-21-4979-2021)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 9042, 9043, 50542, 50543, 75241, 75242, 75243, 75244, 75245, 75246, 75247, 75248, 75249, 75250, 75251, 75252, 75253, 75254, 75255, 75256, 75257, 75258, 75259, 75260, 75261, 75262, 75263, 75264, 75265, 75266, 75267, 75268, 75269, 75270, 75271, 75272, 75273 ], "vocabularyKeywords": [], "identifier_set": [ 12828 ], "observationcollection_set": [ { "ob_id": 24737, "uuid": "31ae96f9cfc54ef9a38638f8723a1d17", "short_code": "coll", "title": "ACSIS: Data collected during the The North Atlantic Climate System Integrated Study.", "abstract": "This data collection includes a range of data collected for The North Atlantic Climate System Integrated Study: ACSIS, including: In-situ airborne observations by the FAAM BAE-146 aircraft, groundbased air composition measurements from Penlee Observatory, and Atlantic Ocean Sea Surface Temperature (SST) studies." } ], "responsiblepartyinfo_set": [ 201717, 201718, 201719, 201720, 201721, 201722, 201727, 202119, 201728, 201729, 201730, 201731, 201732, 201733 ], "onlineresource_set": [ 86085, 86086 ] }, { "ob_id": 41419, "uuid": "6068c32dd69c442881925607c0ddea7c", "title": "Global 3-D TOMCAT CTM Tropospheric Ozone Simulations", "abstract": "Global 3-D simulations of tropospheric ozone (TO3) from the TOMCAT chemical transport model (1996-2016) output at two satellite local solar overpass times (09.30 & 13.30 LST). The ozone data (2-daily overpass times) is provided as 3-D fields on the 2.8°×2.8° horizontal model grid. Atmospheric pressure (Pa) and temperature (K) have also included to provide vertical information on the ozone distribution up to 10 hPa. The ozone data has units of mass mixing ratio (1.0E-9). This work was undertaken primarily as long-term science activities of the NERC - National Centre for Earth Observation.", "creationDate": "2024-01-31T16:51:37.707708", "lastUpdatedDate": "2024-01-31T16:51:37.707714", "latestDataUpdateTime": "2024-01-31T16:51:37.707718", "updateFrequency": "notPlanned", "dataLineage": "These TOMCAT simulations are from the most recent version of the model building on the work of:\r\n\r\nChipperfield (2007): https://rmets.onlinelibrary.wiley.com/doi/10.1256/qj.05.51\r\nRichards et al. (2013): https://acp.copernicus.org/articles/13/2331/2013/\r\nMonks et al. (2017): https://gmd.copernicus.org/articles/10/3025/2017/\r\nRowlinson et al. (2019): https://acp.copernicus.org/articles/19/8669/2019/\r\n\r\nOverall, these TOMCAT simulations were produced by a project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Tropospheric Ozone,Ozone,Air Quality,TOMCAT", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4130, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11538, "startTime": "1996-01-01T00:00:00", "endTime": "2016-12-31T00:00:00" }, "resultQuality": { "ob_id": 4517, "explanation": "Model output has been checked for CF convention compliance, otherwise supplied as-is.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-31" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201882, 201883, 201884, 201885, 201886, 201887, 201888, 201889, 201890, 201891, 201892, 201893 ], "onlineresource_set": [] }, { "ob_id": 41420, "uuid": "87afd9f974c6424290435bd9a418f71a", "title": "DCMEX: meteorological data from Langmuir ground station for the New Mexico field campaign 2022", "abstract": "Meteorological data was collected by the University of Manchester at Langmuir ground station in the Magdalena Mountains, New Mexico, between July and August 2022 as part of the Deep Convective Microphysics Experiment (DCMEX) project.\r\n\r\nThis dataset contains meteorological data from two ground meteorological stations. A Gill MaxiMet GMX600 meteorological station was installed at the Langmuir Laboratory main building (33.97495N, 107.18100W, ~3255 m) next to the Digitel aerosol filter sampler and provides measurements of wind speed, wind direction, temperature, humidity, pressure, and precipitation rate. A second meteorological station, a Vaisalla WTX536 meteorological station, was installed at the Langmuir Kiva-2 laboratory next to the Digitel aerosol filter sampler and provides measurements of wind speed, wind direction, temperature, humidity, pressure, and precipitation rate. The meteorological stations are supported by the University of Manchester scientists.", "creationDate": "2024-01-31T20:15:36.520545", "lastUpdatedDate": "2024-01-31T20:21:54", "latestDataUpdateTime": "2024-05-02T01:48:38", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the University of Manchester instrument scientist before preparation for archiving and delivery to CEDA.", "removedDataReason": "", "keywords": "wind speed, wind direction, temperature, humidity, pressure, and precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-06T09:35:24", "doiPublishedTime": "2024-05-01T08:54:20.111494", "removedDataTime": null, "geographicExtent": { "ob_id": 4136, "bboxName": "", "eastBoundLongitude": -107.1307, "westBoundLongitude": -107.188, "southBoundLatitude": 33.9913, "northBoundLatitude": 34.0503 }, "verticalExtent": null, "result_field": { "ob_id": 41432, "dataPath": "/badc/dcmex/data/DCMEX2022_Ground_Met/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4333090, "numberOfFiles": 5, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11544, "startTime": "2022-07-16T00:00:00", "endTime": "2022-08-10T00:00:00" }, "resultQuality": { "ob_id": 4518, "explanation": "The data are supplied as-is with no quality control applied by Centre for Environmental Data Analysis", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-31" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41430, "uuid": "04efe35bdce34d6d82904fe10fbe8e00", "short_code": "acq", "title": "Aquisition for DCMEX ground-based Meteorological measurement", "abstract": "Meteorological data from two ground meteorological stations. A Vaisalla WTX536 meteorological station was installed at the Kiva-2 laboratory. It was placed on the aerosol sampling mast to provide collocated wind speed, wind direction, temperature, relative humidity, pressure, rainfall rate, and hail rate. A second meteorological station, a Gill MaxiMet GMX600 Met Station, was installed at the Langmuir Laboratory next to the Digitel aerosol filter sampler and provides measurements of wind speed, wind direction, temperature, humidity, pressure, and precipitation rate. The meteorological stations are supported by the University of Manchester scientists." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 84648, 84649, 84650, 84651, 84652, 84653, 84654, 84655, 84656, 84657, 84658, 84659, 84660, 84661, 84662, 84663 ], "vocabularyKeywords": [], "identifier_set": [ 12864 ], "observationcollection_set": [ { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 201894, 201895, 201896, 201897, 201898, 201899, 201904, 202008, 201905 ], "onlineresource_set": [] }, { "ob_id": 41421, "uuid": "3acecae819b84507ad4d62f87cf35155", "title": "Met Office Wind-Driven Rain (WDR)", "abstract": "This dataset contains the annual index of wind-driven rain (sum of all wind-driven rain spells in each year) derived from the UK Climate Projections (UKCP18) for a range of future global warming levels provided on a 5 km British National Grid (BNG). The annual index is calculated for eight wall orientations corresponding to the cardinal and ordinal points of the compass. \r\n\r\nWind-driven rain occurs when falling rain is blown by a horizontal wind so that it falls diagonally towards the ground. The annual index of wind-driven rain is the sum of all wind-driven rain spells for a given wall orientation and time period. It’s measured as the volume of rain blown from a given direction in the absence of any obstructions, with units of litres per square metre per year.\r\n\r\nWind-driven rain is calculated from hourly weather and climate data using an industry-standard formula from ISO 15927–3:2009, which is based on the product of wind speed and rainfall totals. Wind-driven rain is only calculated if the wind would strike a given wall orientation. A wind-driven rain spell is defined as a wet period separated by at least 96 hours with little or no rain (below a threshold of 0.001 litres per m2 per hour).\r\n\r\nThe annual index of wind-driven rain is calculated for a baseline (historical) period of 1981-2000 (corresponding to 0.61°C warming) and for global warming levels of 2.0°C and 4.0°C above the pre-industrial period (defined as 1850-1900). The warming between the pre-industrial period and baseline is the average value from six datasets of global mean temperatures available on the Met Office Climate Dashboard: https://climate.metoffice.cloud/dashboard.html.\r\n\r\nThe magnitudes of 1 in 3 year wind-driven rain spells (i.e. wet spells that would be expected to occur, on average, once every three years) are used to divide the UK into four zones in Approved Document C of the buildings regulations. The magnitudes of 1 in 3 year wind-driven rain spells were calculated for the baseline period (1981-2000) and 20-year periods corresponding to 2°C and 4°C of warming. The magnitudes of all wet spells (here, sum of hourly values of the wind-driven rain metric, I) were calculated, and the largest wet spell in each year was found (in the accompanying report, the magnitude of a wet spell is given the symbol Is' [\"Is prime\"] and has units of litres per metre-squared per spell). For each time period, the largest spells in all years and ensemble members were pooled together. A Gumbel distribution was fitted to the pooled data and used to estimate the magnitude of the 1 in 3 year wet spells across the UK.\r\n\r\nWind-driven rain is required for buildings standards. It is a major source of moisture in walls. Areas subject to very high levels of wind-driven rain may not be suitable for cavity-wall insulation. Under certain circumstances, cavity-wall insulation can enhance the transfer of moisture through walls to the inside of a building causing mould and damp problems.", "creationDate": "2024-02-01T10:29:07.817113", "lastUpdatedDate": "2024-02-01T10:20:34", "latestDataUpdateTime": "2025-02-12T09:05:36", "updateFrequency": "", "dataLineage": "Wind-driven rain was derived from hourly climate data produced as part of the UKCP18 Local climate projections. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "wind-driven rain, Met Office, UKCP18, DESNZ", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "5 km", "status": "completed", "dataPublishedTime": "2025-01-09T13:49:29", "doiPublishedTime": "2025-01-20T09:37:35.881054", "removedDataTime": null, "geographicExtent": { "ob_id": 4137, "bboxName": "UK: Met Office wind-driven rain dataset", "eastBoundLongitude": 3.41, "westBoundLongitude": -12.8, "southBoundLatitude": 49.31, "northBoundLatitude": 60.53 }, "verticalExtent": null, "result_field": { "ob_id": 41582, "dataPath": "/badc/deposited2023/wind_driven_rain", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1049280170, "numberOfFiles": 40, "fileFormat": "The data are provided in NetCDF." }, "timePeriod": { "ob_id": 11546, "startTime": "1980-12-01T00:00:00", "endTime": "2077-11-01T23:59:59" }, "resultQuality": { "ob_id": 4519, "explanation": "The data produced were compared with previously published values and those derived from weather stations.", "passesTest": true, "resultTitle": "Met Office Wind-driven rain data Quality Statement", "date": "2024-02-01" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41668, "uuid": "4e0c93f090dc4b759de8ead14724a35b", "short_code": "comp", "title": "Computation for Wind-Driven Rain", "abstract": "Wind-driven rain is calculated from hourly weather and climate data using an industry-standard formula from ISO 15927–3:2009, which is based on the product of wind speed and rainfall totals. Wind-driven rain is only calculated if the wind would strike a given wall orientation. A wind-driven rain spell is defined as a wet period separated by at least 96 hours with little or no rain (below a threshold of 0.001 litres per m2 per hour).\r\n\r\nThe annual index of wind-driven rain is calculated for a baseline (historical) period of 1981-2000 (corresponding to 0.61°C warming) and for global warming levels of 2.0°C and 4.0°C above the pre-industrial period (defined as 1850-1900). The warming between the pre-industrial period and baseline is the average value from six datasets of global mean temperatures available on the Met Office Climate Dashboard: https://climate.metoffice.cloud/dashboard.html.\r\n\r\nThe magnitudes of 1 in 3 year wind-driven rain spells (i.e. wet spells that would be expected to occur, on average, once every three years) were calculated for the baseline period (1981-2000) and 20-year periods corresponding to 2°C and 4°C of warming. The magnitudes of all wet spells (here, sum of hourly values of the wind-driven rain metric, I) were calculated, and the largest wet spell in each year was found (in the accompanying report, the magnitude of a wet spell is given the symbol Is' [\"Is prime\"] and has units of litres per metre-squared per spell). For each time period, the largest spells in all years and ensemble members were pooled together. A Gumbel distribution was fitted to the pooled data and used to estimate the magnitude of the 1 in 3 year wet spells across the UK." }, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41422, "uuid": "3c030228dab647acb95d9f5bea7879cf", "short_code": "proj", "title": "Met Office Wind-Driven Rain (WDR)", "abstract": "The Climate Change Risk Assessment 3 (CCRA3) identified several different climate hazards that can damage building fabric. The integrity of buildings is affected by moisture ingress, which can cause mould, materials decay and frost damage. In extreme cases, excess moisture accumulation within the building fabric can lead to mechanical failure of the structure. Wind-driven rain (WDR) is the primary source of moisture load for walls. This risk requires further investigation.\r\n\r\nISO 15927-3:2009 specifies two procedures for estimating the quantity of water likely to impact on a wall of any given orientation. This information was based on an older standard (BS 8104:1992) which in turn used rainfall annual exposure maps published in 1976. It is believed these maps were created using data from a small number of weather stations. This information therefore requires updating.\r\n\r\nWind-driven rain can be quantified using a metric I from ISO 15927-3:2009 which has units of volume per unit area and time (here, litres m-2 s-1). This metric was calculated on hourly timescales and is a function of rainfall amount, wind speed, wind direction and wall orientation. Larger values of the metric imply more wind-driven rain striking a wall. The equation which will be used to calculate I yields the amount of WDR passing through a vertical surface in an undisturbed air stream. It does not take into account of any effects of local topography or the building itself on the air flow and hence the amount of water striking a surface.\r\n\r\nThe UKCP18 climate projections include an ensemble of 12 simulations executed at a resolution of 2.2 km, referred to as UKCP Local. These data have been aggregated to a 5 km grid on the OS National grid; the 5 km data were used for this project. Hourly rainfall totals, wind speeds and wind directions are available from UKCP18 Local." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 11484, 11485, 11486, 51184, 51185, 51186, 51187, 51188, 51189, 62353, 75237, 75238, 75239, 75240 ], "vocabularyKeywords": [], "identifier_set": [ 13224 ], "observationcollection_set": [ { "ob_id": 41667, "uuid": "457a3c58a8f04b4d973e15708fe4e868", "short_code": "coll", "title": "Department for Energy Security and Net Zero (DESNZ) Research", "abstract": "This data collection contains environmental datasets produced through research projects funded by the Department for Energy Security and Net Zero (DESNZ)." } ], "responsiblepartyinfo_set": [ 201906, 201907, 201908, 201909, 201910, 201911, 201912, 201913, 201917, 201914, 201915, 201916 ], "onlineresource_set": [ 88253 ] }, { "ob_id": 41425, "uuid": "c14874e943cc453a8e63ce5841ecc9b0", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRFP (RemoTeC) full physics retrieval algorithm (CH4_GO2_SRFP), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-12T15:43:15", "latestDataUpdateTime": "2024-03-09T03:22:24", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project. These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI+) programme.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, atmosphere, methane, CH4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-19T12:06:18", "doiPublishedTime": "2024-03-08T09:45:59.256938", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41493, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_GO2_SRFP/v2.0.2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2034850071, "numberOfFiles": 1001, "fileFormat": "The data are in netCDF format." }, "timePeriod": { "ob_id": 11547, "startTime": "2019-02-05T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3550, "explanation": "For further information see the End to End ECV Uncertainty budget report", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41490, "uuid": "0850cc3803a34fb8b8e70f5f1fec9469", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRFP and CO2_GOS_SRFP v2.0.2 products", "abstract": "The CH4_GO2_SRFP and CO2_GO2_SRFP v2.0.2 products were derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRON-RemoTeC retrieval algorithm." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55387, 55533, 59154, 59155, 61795, 66431, 66432, 66433, 66434, 66435, 66436, 66439, 66440, 66441, 66442, 66443, 66444, 66445, 66446, 66449, 66450, 66451, 66452, 66453, 66454, 66455, 66456, 66457, 66458, 66459, 66461, 66462, 66463, 66464, 66465, 66466, 66467 ], "vocabularyKeywords": [], "identifier_set": [ 12849 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 201939, 201940, 201941, 201942, 201943, 201944, 201945, 202163, 201938, 201950 ], "onlineresource_set": [ 86016, 86017, 86019, 86018, 86020, 86021 ] }, { "ob_id": 41427, "uuid": "875f25069b5d4bd9a7101ca1206ee4f0", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged carbon dioxide from GOSAT-2, derived using the SRFP (RemoTeC) full physics algorithm (CO2_GO2_SRFP), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Full Physics (SRFP) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-12T15:59:24", "latestDataUpdateTime": "2024-03-09T03:22:29", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project. 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Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55387, 55531, 55533, 59154, 59155, 61795, 66430, 66431, 66432, 66433, 66434, 66435, 66436, 66437, 66438, 66439, 66440, 66441, 66442, 66443, 66444, 66445, 66446, 66447, 66448, 66449, 66450, 66451, 66452, 66453, 66454, 66455, 66456, 66457, 66458, 66459, 66460 ], "vocabularyKeywords": [], "identifier_set": [ 12848 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 201955, 201956, 201957, 201958, 201960, 201953, 201954, 202162, 201959, 201965 ], "onlineresource_set": [ 86023, 86024, 86025, 86022, 86026, 86027 ] }, { "ob_id": 41429, "uuid": "3fc7927499fa49e0b6ace6c807972259", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from GOSAT-2, generated with the SRPR (RemoTeC) proxy retrieval algorithm (CH4_GO2_SRPR), version 2.0.2", "abstract": "This dataset contains column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using Near Infrared (NIR) and Shortwave Infrared (SWIR) spectra acquired from the Thermal and Near Infrared Sensor for Carbon Observations - Fourier Transform Spectrometer-2 (TANSO-FTS-2) onboard the Japanese Greenhouse gases Observing Satellite (GOSAT-2), using the Remote Sensing of Greenhouse Gases for Carbon Cycle Modeling (RemoTeC) SRON Proxy (SRPR) retrieval algorithm. Results are provided for the individual GOSAT-2 spatial footprints.\r\n\r\nThese data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI) programme.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-10-12T15:28:38", "latestDataUpdateTime": "2024-03-09T03:22:26", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project. These data have been produced as part of the European Space Agency (ESA)'s Climate Change Initiative (CCI+) programme.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, atmosphere, methane, CH4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-19T12:06:28", "doiPublishedTime": "2024-03-08T09:46:11.965681", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41494, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_GO2_SRPR/v2.0.2", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1534628744, "numberOfFiles": 1001, "fileFormat": "The data are provided in netCDF format." }, "timePeriod": { "ob_id": 11549, "startTime": "2019-02-05T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3550, "explanation": "For further information see the End to End ECV Uncertainty budget report", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-09-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41491, "uuid": "ccc3c9032d97431dbac5052b44545cdd", "short_code": "cmppr", "title": "Composite process for the ESA Greenhouse Gases Climate Change Initiative CH4_GO2_SRPR v2.0.2 product", "abstract": "The CH4_GO2_SRPR v2.0.2 product was derived from data from the TANSO-FTS/2 instrument on the GOSAT satellite, using the SRON-RemoTeC retrieval algorithm." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55387, 55533, 59154, 59155, 61795, 66431, 66432, 66433, 66434, 66436, 66442, 66443, 66444, 66445, 66446, 66449, 66450, 66451, 66452, 66453, 66454, 66455, 66456, 66457, 66458, 66459, 66461, 66462, 66463, 66464, 66465, 66466, 66467, 66468, 66469, 66470, 66471, 66472, 66473, 66474, 66475 ], "vocabularyKeywords": [], "identifier_set": [ 12850 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 201975, 201969, 201970, 201971, 201972, 201973, 201974, 202164, 201968, 201980 ], "onlineresource_set": [ 86033, 86029, 86031, 86028, 86030, 86032 ] }, { "ob_id": 41433, "uuid": "b153fcf4ad4944baa059e433c83255c8", "title": "Fixed concentration simulations with UKESM1 across multiple global warming levels (500 years per simulation)", "abstract": "Six 500-year long climate model simulations were performed with the UKESM1-0-LL Earth System Model under constant external forcing conditions. The simulations were designed to span a range of global warming levels to explore projected climate change between 1.5 and 5°C above pre-industrial levels in a fully coupled, state-of-art Earth System Model (ESM). \r\nThe external forcings for each simulation are held at the level they were at in a parent ScenarioMIP simulation at the time the simulations are branched off. The six experiments are branched from the historical and ScenarioMIP experiments as follows (in order of mean warming over 500 years): \r\n•r1i1p1f2 (suite-id: u-bq777): 2014 in historical \r\n•r3i1p1f2 (suite-id: u-bw848): 2020 in SSP3-7.0 \r\n•r5i1p1f2 (suite-id: u-bz227): 2025 in SSP3-7.0 \r\n•r6i1p1f2 (suite-id: u-cd269): 2025 in SSP2-4.5 \r\n•r4i1p1f2 (suite-id: u-bw987): 2030 in SSP1-1.9\r\n•r2i1p1f2 (suite-id: u-bu607): 2040 in SSP3-7.0\r\nThe dataset contains a subset of CF-compliant, CMORised monthly model output, with more variables and higher frequency data to be added.\r\nFilenaming convention example: \r\n[varname]_[miptable]_UKESM1-0-LL_futControl_r5i1p1f2_gn_245001-252412.nc\r\nPaper reference: To be inserted here when available. \r\nNote futControl is used to reflect the parallels to piControl (fixed external forcings, but future conditions).\r\nThe NERC grants linked to this dataset are: NE/N018486/1 and NE/N018591/1", "creationDate": "2024-02-02T12:28:00.723459", "lastUpdatedDate": "2024-02-02T12:24:52", "latestDataUpdateTime": "2024-03-09T03:22:24", "updateFrequency": "", "dataLineage": "The model simulations were run by Andrea Dittus on MonSooN2. Alistair Sellar helped with generating the required time-slice ancillaries. The data were post-processed by Andrea Dittus using the Met Office software CDDS. The data were CMORised using the Met Office CDDS software.\r\n\r\nData were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "UKESM", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-02-09T16:13:14", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41434, "dataPath": "/badc/deposited2024/REAL-world-climate-change-proj/data/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 137284716458, "numberOfFiles": 566, "fileFormat": "These data are provided in netCDF format." }, "timePeriod": { "ob_id": 11550, "startTime": "2015-01-01T00:00:00", "endTime": "2539-12-30T23:59:59" }, "resultQuality": { "ob_id": 4520, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41436, "uuid": "e30c1e20f6fd4b96a33b4f9a9ceb2fa5", "short_code": "comp", "title": "Fixed concentration experiments with UKESM1-0-LL", "abstract": "The external forcings for each simulation are held at the level they were at in a parent ScenarioMIP simulation at the time the simulations are branched off. In the case of GHGs, that corresponds to constant concentrations and for anthropogenic aerosols it corresponds to constant emissions. The vegetation is dynamic, but each grid cell has a prescribed crop and pasture fraction based on the conditions of their parent simulations at the point of branching-off. Solar and volcanic forcing are set to the forcings used in the pre-industrial control simulations. All simulations are branched from the first initial condition realisation of the parent ScenarioMIP experiment (r1i1p1f2 in CMIP6 nomenclature)." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41435, "uuid": "9550e4ab351a46689bce9093089f0828", "short_code": "proj", "title": "Robust Projections of Real-World Climate Change", "abstract": "Climate change is one of the leading global challenges facing society and the planet. Predicting how the climate will change as human activities lead to emission of more greenhouse gases is a global scientific challenge for climate scientists. We use models of the climate to make predictions. Because of limitations in computing power, and because of gaps in our understanding of the climate, these models are not perfect. Predictions from the models are, therefore, also not perfect. We are faced by the huge challenge of extracting robust information from climate models about how real-world climate will change in the future under specified scenarios of different greenhouse gas emissions. Such projections are central to leading climate change assessments, such as those produced by the Intergovernmental Panel on Climate Change (IPCC). This project will provide a step-change in the ability of climate scientists to produce robust projections of climate change and to quantify the uncertainties in projections. A new framework will be developed that combines information from models, observations and our basic understanding of climate with modern statistical techniques to produce projections. This new framework will be applied to three important climate regimes of Earth: tropical and subtropical temperature and precipitation change; middle-latitude cyclones and anti-cyclones; and polar temperature and sea-ice changes. We will bring together leading UK scientists (many are IPCC authors) from the Universities of Exeter, Reading, Oxford and East Anglia, and the Met Office, to address this grand challenge in climate science. We aim to precipitate a cultural shift that unifies diverse approaches from techniques to understand climate process and statistical methods and consolidate the UKs position as a world-leading centre for climate projection science.\r\nThe NERC grants linked to this project are: NE/N018486/1 and NE/N018591/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 6255, 11046, 50415, 50417, 50418, 50426, 50429, 50431, 50468, 50475, 50496, 50498, 50554, 50555, 50564, 50566, 50568, 50597, 50598, 50599, 50611, 50614, 50618, 50620, 52746, 54216, 60438 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 201982, 201983, 201984, 201985, 201986, 201987, 201988, 202050, 201989, 202051, 201990 ], "onlineresource_set": [] }, { "ob_id": 41437, "uuid": "77a0e1e3bcbb49a5b4c89fe9cc90a788", "title": "DCMEX: aerosol number-size distribution data from Langmuir ground station for the New Mexico field campaign 2022 - Version 1.0", "abstract": "Aerosol number-size distribution data collected by the University of Manchester at the Kiva-2 site at Langmuir ground station in the Magdalena Mountains, New Mexico, between July and August 2022 as part of the Deep Convective Microphysics Experiment (DCMEX) project. Instrument supplied by the National Centre for Atmospheric Science - Atmospheric Measurement and Observation Facility (NCAS-AMOF).\r\n\r\nThis version 1.0 dataset contains the aerosol size distribution data from the Kiva-2 site at Langmuir Laboratory. A GRIMM Optical Particle Counter (OPC) model 1.108 was installed at the Langmuir Laboratory Kiva-2 site (33.97495N, 107.18100W, ~3255 m). The GRIMM OPC was supported by AMOF and the University of Manchester scientists.", "creationDate": "2024-01-31T20:15:36.520545", "lastUpdatedDate": "2024-01-31T20:21:54", "latestDataUpdateTime": "2024-09-11T13:12:13", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the University of Manchester and National Centre for Atmospheric Science (NCAS) Atmospheric Measurement and Observation Facility (AMOF) instrument scientists before preparation for archiving and delivery to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "aerosol, ncas-amof, ncas-opc-1", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-06T09:42:45", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4139, "bboxName": "Kiva-2 site, Langmuir Laboratory, New Me", "eastBoundLongitude": -107.188, "westBoundLongitude": -107.188, "southBoundLatitude": 33.9913, "northBoundLatitude": 33.9913 }, "verticalExtent": null, "result_field": { "ob_id": 42896, "dataPath": "/badc/ncas-mobile/data/ncas-opc-1/20220715_dcmex/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 144628104, "numberOfFiles": 27, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11551, "startTime": "2022-07-15T00:00:00", "endTime": "2022-08-09T23:59:59" }, "resultQuality": { "ob_id": 4518, "explanation": "The data are supplied as-is with no quality control applied by Centre for Environmental Data Analysis", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-01-31" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41439, "uuid": "cbd2046e1bf041b2824fe2124105cb05", "short_code": "acq", "title": "Aquisition for DCMEX ground-based aerosol measurement", "abstract": "Aerosol number-size distribution data collected by a GRIMM Optical Particle Counter (OPC) model 1.108 installed at the Langmuir Laboratory Kiva-2 site (33.9913°N, 107.1880°W) in the Magdalena Mountains, New Mexico between July and August 2022 as part of the Deep Convective Microphysics Experiment (DCMEX) project." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38131, "uuid": "7231e85e8ec34e36a6d2815d7edf2330", "short_code": "proj", "title": "Deep Convective Microphysics Experiment (DCMEX)", "abstract": "The goal of the Deep Convective Microphysics Experiment (DCMEX) project is to ultimately reduce the uncertainty in equilibrium climate sensitivity by improving the representation of microphysical processes in global climate models (GCMs). It is the anvils produced by tropical systems in particular, that contribute significantly to cloud feedbacks. The anvil radiative properties, lifetimes and areal extent are the key parameters. DCMEX will determine the extent to which these are influenced, or even controlled by the cloud microphysics including the habits, concentrations and sizes of the ice particles that make up the anvils, which in turn depend on the microphysical processes in the mixed-phase region of the cloud as well as those occurring in the anvil itself.\r\n\r\nA measurement campaign took place in July-August 2022 over the Magdalena mountains, New Mexico. The FAAM BAe-146 aircraft, dual-polarisation doppler radar, aerosol instruments and stereo-camera observations collected data which was then combined with modelling activities to improve the representation of deep convective microphysics within climate models." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 53936, 53937, 53938, 53939, 53940, 53941, 53942, 53943, 53944, 74110, 75052, 75061, 75236 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 38137, "uuid": "b1211ad185e24b488d41dd98f957506c", "short_code": "coll", "title": "DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment", "abstract": "A collection of measurements made for the Deep Convective Microphysics Experiment (DCMEX) project. This includes in-situ airborne observations by the FAAM BAE-146 aircraft, cloud images from 2 NCAS cameras deployed at 3 sites in the area during the course of the field campaign and meteorological and aerosol measurements made at two groundbased stations.\r\n\r\nDCMEX examined the formation and development of clouds over mountains and was based in the Magdalena Mountains, New Mexico area, between July and August 2022.\r\n\r\nAssociated datasets are also available: \r\nTimelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign https://doi.org/10.5281/zenodo.7756710 \r\nDCMEX ground based radar data https://doi.org/10.5281/zenodo.10472266 \r\nand, the aircraft ice nucleating particle filter analysis https://doi.org/10.5518/1476" } ], "responsiblepartyinfo_set": [ 201995, 201996, 201997, 201998, 201999, 202000, 202007, 202003, 202002, 202006 ], "onlineresource_set": [ 86040 ] }, { "ob_id": 41440, "uuid": "a35041dacc534ceeb8f581296d2feb6b", "title": "BICEP/NCEO: Marine phytoplankton carbon OC-CCI v4.2 monthly composites, 9km resolution, 1998-2020 ", "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020a and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). \r\n\r\nData are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation are also provided.", "creationDate": "2024-02-06T10:53:13.170831", "lastUpdatedDate": "2024-02-06T10:53:13.170837", "latestDataUpdateTime": "2024-02-06T10:53:13.170841", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO). Further details on the used methods can be found in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "removedDataReason": "", "keywords": "Phytoplankton,Carbon,Ocean Colour", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4140, "bboxName": "", "eastBoundLongitude": -180.0, "westBoundLongitude": 180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11552, "startTime": "1998-01-01T00:00:00", "endTime": "2020-12-31T00:00:00" }, "resultQuality": { "ob_id": 4521, "explanation": "The satellite-based estimates of phytoplankton carbon are consistent with in situ data (Figure 7, Sathyendranath et al. 2020) and the theoretical model used to generate the products compared well with results from earlier published empirical models (Figure 8, Sathyendranath et al. 2020). Further details are provided in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41443, "uuid": "cb9e27db854d45c4a7e8417b175e5c1d", "short_code": "cmppr", "title": "Composite Process for BICEP/NCEO: Marine phytoplankton carbon OC-CCI v4.2 monthly composites, 9km resolution, 1998-2020 ", "abstract": "Composite process covering Acquisition for: BICEP/NCEO: Marine phytoplankton carbon OC-CCI v4.2 monthly composites, 9km resolution, 1998-2020 and Marine phytoplankton carbon." }, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 202009, 202010, 202011, 202012, 202013, 202014, 202015, 202016, 202017, 202018, 202019, 202020, 202021, 202022, 202023 ], "onlineresource_set": [ 86034 ] }, { "ob_id": 41444, "uuid": "bb21fcb130754f02b322e6662bba743b", "title": "BICEP/NCEO: Marine phytoplankton carbon OC-CCI v4.2 monthly composites, 9km resolution, 1998-2020", "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020a and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). \r\n\r\nData are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation are also provided.", "creationDate": "2024-02-06T10:58:46.432227", "lastUpdatedDate": "2024-02-06T10:58:46.432233", "latestDataUpdateTime": "2024-02-06T10:58:46.432237", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO). Further details on the used methods can be found in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "removedDataReason": "", "keywords": "", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4141, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": 180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11553, "startTime": "1998-01-01T00:00:00", "endTime": "2020-12-31T00:00:00" }, "resultQuality": { "ob_id": 4522, "explanation": "The satellite-based estimates of phytoplankton carbon are consistent with in situ data (Figure 7, Sathyendranath et al. 2020) and the theoretical model used to generate the products compared well with results from earlier published empirical models (Figure 8, Sathyendranath et al. 2020). Further details are provided in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 31968, "uuid": "cd161a8305384eb38109036a74d7e2b9", "short_code": "proj", "title": "ESA Biological Pump and Carbon Export Processes (BICEP) Project", "abstract": "The ESA Biological Pump and Carbon Export Processes (BICEP) project is an ESA project led by the Plymouth Marine Laboratory. The objective of the BICEP project is to further advance our capacity to better characterise the different components of the ocean biological carbon pump, its pools and fluxes, its variability in space and time and the understanding of its processes and interactions with the earth system, from a synergetic use of space data, in-situ measurements and model outputs.\r\n\r\nThe development of the BICEP datasets was also supported the Simons Foundation grant 'Computational Biogeochemical Modeling of Marine Ecosystems' (CBIOMES, number 549947)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 202027, 202028, 202029, 202030, 202031, 202032, 202033, 202034, 202035, 202036, 202037, 202038, 202039, 202040, 202041, 202042 ], "onlineresource_set": [ 86035 ] }, { "ob_id": 41445, "uuid": "9b856433535146828a515b1125bc8a07", "title": "BICEP/NCEO: Marine Phytoplankton Carbon OC-CCI v4.2 monthly composites, 9km resolution, 1998-2020", "abstract": "A spectrally-resolved photoacclimation model was unified with a primary production model that simulated photosynthesis as a function of irradiance using a two-parameter photosynthesis versus irradiance (P-I) function to estimate the carbon content of marine phytoplankton based on ocean-colour remote sensing products (Sathyendranath et al. 2020a and references therein for details). The photoacclimation model contains a maximum chlorophyll-to-carbon ratio for three different phytoplankton size classes (pico-, nano- and microphytoplankton) that was inferred from field data, as in Sathyendranath et al. (2020). \r\n\r\nData are provided as netCDF files containing carbon products for pico-, nano- and microphytoplankton (C_picophyto, C_nanophyto and C_microphyto, respectively, in mg C m-3) and the total phytoplankton community (C_phyto in mg C m-3) for the period of 1998 to 2020 at 9 km spatial resolution. Additional variables that were used for the calculation are also provided.", "creationDate": "2024-02-06T11:03:19.769137", "lastUpdatedDate": "2024-02-06T11:03:19.769144", "latestDataUpdateTime": "2024-02-06T11:03:19.769148", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the Plymouth Marine Laboratory and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The research underpinning the work was supported by the European Space Agency (ESA) Biological Pump and Carbon Export Processes (BICEP) project and the product generation was supported by the National Centre for Earth Observation (NCEO). Further details on the used methods can be found in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "removedDataReason": "", "keywords": "", "publicationState": "working", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "pending", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4142, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 11554, "startTime": "1998-01-01T00:00:00", "endTime": "2020-12-31T00:00:00" }, "resultQuality": { "ob_id": 4523, "explanation": "The satellite-based estimates of phytoplankton carbon are consistent with in situ data (Figure 7, Sathyendranath et al. 2020) and the theoretical model used to generate the products compared well with results from earlier published empirical models (Figure 8, Sathyendranath et al. 2020). Further details are provided in Sathyendranath et al. (2020).\r\n\r\nSathyendranath, S.; Platt, T.; Kovač, Ž.; Dingle, J.; Jackson, T.; Brewin, R.J.W.; Franks, P.; Marañón, E.; Kulk, G.; Bouman, H.A. Reconciling models of primary production and photoacclimation. Applies Optics, 2020, 59, C100. doi.org/10.1364/AO.386252", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30128, "uuid": "7fe9f59731ab47b6a20e792e0cba4641", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) partnered datasets", "abstract": "The National Centre for Earth Observation (NCEO) has a proud tradition of being involved with some of the most successful international collaborations in the Earth observation. This Collection contains dataset generated and/or archived with the support of NCEO resource or scientific expertise. Some notable collaboration which generated data within this collection are as follows:\r\n\r\nThe European Space Agency (ESA)'s Climate Change Initiative (CCI) program. The program goal is to provide stable, long-term, satellite-based Essential Climate Variable (ECV) data products for climate modelers and researchers.\r\n\r\nThe EUSTACE (EU Surface Temperature for All Corners of Earth) project is produced publicly available daily estimates of surface air temperature since 1850 across the globe for the first time by combining surface and satellite data using novel statistical techniques.\r\n\r\nFIDUCEO has created new climate datasets from Earth Observations with a rigorous treatment of uncertainty informed by the discipline of metrology. This response to the need for enhanced credibility for climate data, to support rigorous science, decision-making and climate services. The project approach was to develop methodologies for generating Fundamental Climate Data Records (FCDRs) and Climate Data Records (CDRs) that are widely applicable and metrologically rigorous. \r\n\r\nThe “BACI” project translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. The trans-disciplinary project offers new insights into the functioning and state of ecosystems and biodiversity. BACI enables the user community to detect abrupt and transient changes of ecosystems and quantify the implications for regional biodiversity.\r\n\r\nThe UK Natural Environment Research Council has established a knowledge transfer network called NCAVEO (Network for Calibration and Validation of EO data - NCAVEO) which has as its aim the promotion and support of methodologies based upon quantitative, traceable measurements in Earth observation. \r\n\r\nThe Geostationary Earth Radiation Budget 1 & 2 instruments (GERB-1 and GERB-2) make accurate measurements of the Earth Radiation Budget. They are specifically designed to be mounted on a geostationary satellite and are carried onboard the Meteosat Second Generation satellites operated by EUMETSAT. They were produced by a European consortium led by the UK (NERC) together with Belgium, Italy, and EUMETSAT, with funding from national agencies.\r\n\r\nGloboLakes analysed 20 years of data from more than 1000 large lakes across the globe to determine 'what controls the differential sensitivity of lakes to environmental perturbation'. This was an ambitious project that was only possible by bringing together a consortium of scientists with complementary skills. These include expertise in remote sensing of freshwaters and processing large volumes of satellite images, collation and analysis of large-scale environmental data, environmental statistics and the assessment of data uncertainty, freshwater ecology and mechanisms of environmental change and the ability to produce lake models to forecast future lake conditions.\r\n\r\nThis SPEI collaboration consists of high spatial resolution Standardized Precipitation-Evapotranspiration Index (SPEI) drought dataset over the whole of Africa at different time scales from 1 month to 48 months. It is calculated based on precipitation estimates from the satellite-based Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and potential evaporation estimates by the Global Land Evaporation Amsterdam Model (GLEAM)." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 202043, 202044, 202045, 202046, 202047, 202048, 202049 ], "onlineresource_set": [ 86036 ] }, { "ob_id": 41456, "uuid": "d32244e674dd438ca4d321560daad755", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Combined river discharge product, v1.0", "abstract": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci).\r\n\r\nThese river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series (called CM) and the water level product derived by radar altimeters (called Altimetry), and Level-3, in which the river discharge products obtained from altimeters and multispectral sensors are used. The river discharges are derived following several approaches:\r\n\r\n1) L2 Merged river discharge:\r\n\r\na) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution. \r\n\r\nb) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry\r\n\r\n\r\n2) L3 Merged river discharge:\r\n\r\na) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": "2024-11-29T02:12:15", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "river discharge, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-11-28T19:11:39", "doiPublishedTime": "2024-11-28T22:58:59", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43214, "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-combined/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 13870715, "numberOfFiles": 151, "fileFormat": "Data are in both CSV and NetCDF format" }, "timePeriod": { "ob_id": 11890, "startTime": "1992-12-24T00:00:00", "endTime": "2023-09-30T23:59:59" }, "resultQuality": { "ob_id": 4605, "explanation": "For information on data quality see the project documentation", "passesTest": true, "resultTitle": "CCI generic", "date": "2024-10-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43213, "uuid": "649334adc75f487bbdce0254185b3093", "short_code": "cmppr", "title": "Composite process for ESA River Discharge Climate Change Initiative (RD_cci): combined river discharge product, v1.0", "abstract": "River discharge (Q) data in cubic meters per second (m3/s) has been computed by the ESA Climate Change Initiative River Discharge project (RD_cci). These river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series and radar altimeters water level product and Level-3, in which the river discharge products obtained from altimeters and multispectral imagers are used. The river discharges are derived following several approaches:\r\n\r\n1) L2 Merged river discharge:\r\n\r\na) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution.\r\n\r\nb) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry\r\n\r\n2) L3 Merged river discharge:\r\n\r\na) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach" }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2612, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 70, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_river_discharge_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41537, "uuid": "dbba9cfe8d104648b19e39f4c2da1a27", "short_code": "proj", "title": "ESA River Discharge Climate Change Initiative (RD_cci)", "abstract": "The ESA river discharge Climate Change Initiative project aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63576, 75233, 75235 ], "vocabularyKeywords": [], "identifier_set": [ 13210 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 202093, 202094, 202095, 202096, 202097, 202098, 205534, 205537, 205535, 205536 ], "onlineresource_set": [ 88210, 88121, 88122, 88123 ] }, { "ob_id": 41457, "uuid": "a8422dd3766c447d8b5fa80920649f31", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2", "abstract": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci). \r\n\r\nThese river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series. These multi-mission, multispectral time series are also referred to as CM. The river discharges are derived following several approaches:\r\n\r\nCalibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;\r\n\r\nCalibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;\r\n\r\nUncalibrated CM approach – CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series.", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": "2024-11-29T02:12:13", "updateFrequency": "", "dataLineage": "Data were produced by the River Discharge CCI project, and supplied to CEDA for archiving as part of the CCI Open Data Portal.", "removedDataReason": "", "keywords": "river discharge, CCI, multispectral", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-11-28T19:12:27", "doiPublishedTime": "2024-11-28T23:02:19", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43215, "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-multi/v1.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10756247, "numberOfFiles": 103, "fileFormat": "Data are in both CSV and NetCDF format" }, "timePeriod": { "ob_id": 11887, "startTime": "1999-10-03T00:00:00", "endTime": "2023-09-30T23:59:59" }, "resultQuality": { "ob_id": 4605, "explanation": "For information on data quality see the project documentation", "passesTest": true, "resultTitle": "CCI generic", "date": "2024-10-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43210, "uuid": "d24707d0fbfa4179bf2c52f652952800", "short_code": "cmppr", "title": "Composite process for the ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2", "abstract": "River discharge (Q) data has been derived in cubic meters per second (m3/s) by the ESA Climate Change Initiative River Discharge project (RD_cci). These multispectral indices-based river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series.\r\n\r\nThe river discharges are derived following several approaches:\r\n\r\nBestFit: by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;\r\n\r\nCopula: by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;\r\n\r\nuncalCDF: by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2612, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 70, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_river_discharge_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41537, "uuid": "dbba9cfe8d104648b19e39f4c2da1a27", "short_code": "proj", "title": "ESA River Discharge Climate Change Initiative (RD_cci)", "abstract": "The ESA river discharge Climate Change Initiative project aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63576, 75233, 75234 ], "vocabularyKeywords": [], "identifier_set": [ 13211 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 202099, 202100, 202101, 202102, 202103, 202104, 205507, 205510, 205511, 205508, 205509 ], "onlineresource_set": [ 88211, 88103, 88104, 88105 ] }, { "ob_id": 41458, "uuid": "44c930e1388f40728884fbdf7e28c109", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0", "abstract": "This dataset comprises the altimetry-based river discharge (RD-ALTI) Climate Research Data Package (CRDP), derived from nadir radar altimeter missions by the ESA CCI River Discharge precursor project (RD_cci). \r\n\r\nIt provides long-term satellite river discharge (RD) time series at specified locations (defined in the \"Selection of river basins\" document, available at https://climate.esa.int/documents/2189/D2_CCI-Discharge-0004-RP_WP2_v1-1.pdf) River discharge (in m3/s) corresponds to the water volume passing through the river cross-section per unit of time. In this dataset, it is computed from a rating curve applied to long-term satellite altimeter water surface elevation (WSE) from https://catalogue.ceda.ac.uk/uuid/c5f0aa806ec444b4a4209b49efc4bb65. The rating curve is obtained by fitting the relationship between in-situ discharge and altimeter WSE with a power law following a Bayesian approach.", "creationDate": "2023-12-06T08:01:27.903402", "lastUpdatedDate": "2023-12-06T08:00:25", "latestDataUpdateTime": "2024-07-30T01:45:50", "updateFrequency": "", "dataLineage": "Data were produced by the River Discharge CCI precursor project team, comprising Hydro Matters, LEGOS, and EOLA.\r\n\r\nData were supplied for archiving at the Centre for Environmental Data Analysis (CEDA) as part of the CCI Open Data Portal in the context of the CCI Knowledge Exchange Project.", "removedDataReason": "", "keywords": "CCI, River Discharge, Altimeter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-07-25T10:43:11", "doiPublishedTime": "2024-07-29T08:43:45", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 43061, "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-ALTI/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6980385, "numberOfFiles": 101, "fileFormat": "Data are provided in both CSV and NetCDF format" }, "timePeriod": { "ob_id": 11812, "startTime": "1992-12-24T00:00:00", "endTime": "2023-09-18T23:59:59" }, "resultQuality": { "ob_id": 4580, "explanation": "For information on the data quality see the project documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-07-02" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 43053, "uuid": "a2fc28adb5724014af898b19e4e24cb1", "short_code": "comp", "title": "Computation for the ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0", "abstract": "Long-term satellite river discharge (RD) time series have been derived at specified locations[1], utilizing the methodology outlined in detail in [2]. \r\n\r\nIn this context, river discharge represents the discharge in cubic meters per second (m3/s) obtained through the rating curve computation facilitated by in-situ discharge and water surface elevation (WSE) merged time series, as computed in [2] over the calibration period (individually defined for each station and summarized in the ATBD [3])\r\n\r\n[1] D.2 Selection of river basins. CCI River Discharge precursor project Document (CCI-Discharge-0004-RP_WP2, Issue 1.0)\r\n[2] D.3. Water Surface Elevation (WL) Algorithm Theoretical Basis Document (ATBD) (CCI-Discharge-0005-ATBD_WL, Issue 1.1)\r\n[3] D.3. River Discharge (Q) from Altimeters and Ancillary data, multispectral images and data combination - Algorithm Theoretical Basis Document (ATBD) (CCI-Discharge-0012-ATBD, Issue 1.1)" }, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2612, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 70, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_river_discharge_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41537, "uuid": "dbba9cfe8d104648b19e39f4c2da1a27", "short_code": "proj", "title": "ESA River Discharge Climate Change Initiative (RD_cci)", "abstract": "The ESA river discharge Climate Change Initiative project aims to derive long term climate data records (at least over 20-years) of river discharge for some selected river basins (and some locations in the river network) using satellite remote sensing observations (altimetry and multispectral images) and ancillary data." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63576, 75231, 75232, 75233 ], "vocabularyKeywords": [], "identifier_set": [ 13158 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 202107, 202108, 202105, 202109, 202106, 202110, 204536, 204592, 204537, 204538, 204539, 204540, 204541, 204542 ], "onlineresource_set": [ 86571, 86572, 86573, 86574, 86575, 86578, 86591, 86592 ] }, { "ob_id": 41468, "uuid": "9c82a38dff8940b594fbbb939cf4b1eb", "title": "Deforestation and Climate Change scenarios with a regional climate model over Borneo (2005 – 2100).", "abstract": "This dataset contains simulations of the climate scenario RCP8.5 with and without total deforestation using the regional climate model RegCM4. The output was then used for calculations of the Fire Weather Index, and that data is also included. \r\nThe data is in two main formats: netcdf files from the regional climate model, and the txt files with Fire Weather Index. \r\nThe time periods covered are: \r\n2010 – 2029 RCP8.5, Control Land cover \r\n2010 – 2029 RCP8.5, Deforested Land cover\r\n2081 – 2099 RCP8.5, Control Land cover\r\n2081 – 2099 RCP8.5, Deforested Land cover\r\nThe last 15 years of each simulation was used. Each simulation was run for each initial conditions and boundary conditions of the models: HadGEM2-ES, MPI-ESM-MR, CSIRO-MK36, IPSL-CM5A-LR, CNRM-CM5, CanESM2, giving a total of 24 simulations.\r\n\r\nThese data are used in the paper, “Future Fire risk under Climate Change and Deforestation Scenarios in Tropical Borneo”. Davies-Barnard et al, 2023.", "creationDate": "2024-02-10T18:45:02.706550", "lastUpdatedDate": "2024-02-10T18:45:31", "latestDataUpdateTime": "2024-02-10T18:45:02", "updateFrequency": "notPlanned", "dataLineage": "Regional climate model simulations produced by the project team before being archived at CEDA", "removedDataReason": "", "keywords": "Regional climate model,fire,deforestation,Borneo,tropical deforestation and climate change", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4146, "bboxName": "", "eastBoundLongitude": 120.6, "westBoundLongitude": 108.67, "southBoundLatitude": -4.46, "northBoundLatitude": 7.65 }, "verticalExtent": null, "result_field": { "ob_id": 41496, "dataPath": "/badc/deposited2024/KaliBorneoClimateProjections", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 56467817667, "numberOfFiles": 91, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11560, "startTime": "2010-01-01T00:00:00", "endTime": "2100-12-31T00:00:00" }, "resultQuality": { "ob_id": 4525, "explanation": "Data are as supplied by the project team. No quality testing undertaken by CEDA", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41497, "uuid": "4bb0a4b1e21d489e8ccb5eadd85330cb", "short_code": "comp", "title": "Computation process for: Deforestation and Climate Change scenarios with a regional climate model over Borneo (2005 – 2100). and RegCM4.", "abstract": "This computation produced simulations of the climate scenario RCP8.5 with and without total deforestation using regional climate model RegCM4. The output was then used for calculations of the Fire Weather Index, and that data is also included.\r\nThe data is in two main formats: netcdf files from the regional climate model, and the txt files with Fire Weather Index.\r\nThe time periods covered are:\r\n2010 – 2029 RCP8.5, Control Land cover\r\n2010 – 2029 RCP8.5, Deforested Land cover\r\n2081 – 2099 RCP8.5, Control Land cover\r\n2081 – 2099 RCP8.5, Deforested Land cover\r\nThe last 15 years of each simulation was used. Each simulation was run for each initial conditions and boundary conditions of the models: HadGEM2-ES, MPI-ESM-MR, CSIRO-MK36, IPSL-CM5A-LR, CNRM-CM5, CanESM2, giving a total of 24 simulations." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 41469, "uuid": "1cddb86e4c714ff1ac71a89fcad45dfa", "short_code": "proj", "title": "The Kalimantan Lestari (KaLi) project", "abstract": "Indonesia's Central Kalimantan province on the island of Borneo is home to extensive peatlands. In dry years such as 2015, peat fires burn for months with huge impacts: Exposure to smoke during this period is expected to cause 100,000 premature deaths, caused major economic disruption with a cost of $16.1Bn to the Indonesian economy and, for three months, emitted more carbon than the entire EU. Indonesia's peatland fires were described as 2015's 'worst environmental disaster' (Guardian, 2015) with Central Kalimantan at the epicentre. The majority of fires in this region are started deliberately, primarily to clear forest for small or large-scale agriculture (satellite data indicates that there were close to 40,000 fire hot spots in C. Kalimantan peatlands in 2015), but their frequency, duration and severity are strongly climate linked and facilitated by El Nino droughts, which may become more frequent under global warming. In their intact natural waterlogged, forested state these peatlands rarely burn, therefore fires are concentrated in the (extensive) areas that have dried to some degree due to deforestation and drainage for agriculture and timber extraction. Here, smouldering fires burn down into the underlying peat, can burn for months and are the primary cause of near annual air pollution events affecting SE Asia, which were particularly severe during 2015. Thus the drivers behind the peatland fires are a combination of climatic processes, a legacy of historic land use impacts that ensure a high fuel load, and human activities that provide ignition sources. The resulting huge impacts are, therefore, to a large extent preventable but effective action requires a more detailed understanding of future climate-associated risk, biophysical and socio-economic conditions and human behaviours. \r\n\r\nThis multidisciplinary project has three core aims: \r\n\r\n1) To better understand the drivers behind the multiple drought- and fire-associated hazards and their spatial distribution in the peatlands of Central Kalimantan Province, Indonesian Borneo\r\n\r\n2) To characterise the multiple, cumulative impacts of drought and the biophysical and human behavioural chains leading to them, and identify the population groups/communities most vulnerable to these hazards. \r\n\r\n3) Combining information from 1 and 2, identify priority actions and policies for work to reduce the risk of fire and identify the socio-cultural, agro-ecological, physical and economic hurdles to achieving positive outcomes from their implementation within the context of sustainable development that leads to better environmental and socio-economic circumstances for all. \r\n\r\nThe ultimate aim of this project is to build long term resilience to the multiple hazards associated with drought and fire in Central Kalimantan's peatlands by developing the knowledge, tools and capacity to reduce the current co-drivers (e.g. human land uses) and also to plan ahead for when circumstances (climate, land use) change in the future." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 12066, 50419, 50468, 50475, 50496, 50564, 50589, 50590, 50591, 50592, 50596, 50600, 52741, 52746, 52747, 52753, 54088, 54228, 54342, 54421, 62645, 75200, 75201, 75202, 75203, 75204, 75205, 75206, 75207, 75208, 75209, 75210, 75211, 75212, 75213, 75214, 75215, 75216, 75217, 75218, 75219, 75220, 75221, 75222, 75223, 75224, 75225, 75226, 75227, 75228, 75229, 75230 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 202127, 202128, 202129, 202130, 202131, 202132, 202133, 202189, 202134, 202135 ], "onlineresource_set": [ 86066 ] }, { "ob_id": 41480, "uuid": "9458b2aea1cf4dd48a7ff810a4197304", "title": "Chamber reaction products identified from toluene/m-xylene oxidation using GCxGC TOFMS at the University of Birmingham", "abstract": "A list of reaction products from the photo-oxidation of m-xylene and toluene in chamber experiments for the Quantitative Attribution of Secondary Organic Aerosol in Beijing to its Precursors project which was part of the Air Pollution and Human Health in Developing Megacities programme.\r\n\r\nA potential aerosol mass (PAM) chamber was used to investigate the oxidised products from the photo-oxidation of m-xylene and toluene. The chamber experiments were carried out with hydroxyl (OH) radical as oxidant in both high- and low-NOx conditions and the resultant aerosol samples were collected using quartz filters and analysed by the two dimensional Gas Chromatography Time-Of-Flight Mass Spectrometry (GC×GC-TOFMS) at the University of Birmingham.", "creationDate": "2024-02-13T12:32:29.766118", "lastUpdatedDate": "2024-02-13T12:15:22", "latestDataUpdateTime": "2024-03-09T03:22:28", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "APHH, aerosol", "publicationState": "published", "nonGeographicFlag": true, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-02-15T14:26:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": null, "verticalExtent": null, "result_field": { "ob_id": 41477, "dataPath": "/badc/aphh/data/beijing/bham-gc-pam/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 10072, "numberOfFiles": 3, "fileFormat": "BADC-CSV" }, "timePeriod": { "ob_id": 11561, "startTime": "2019-01-31T00:00:00", "endTime": "2021-01-31T00:00:00" }, "resultQuality": { "ob_id": 4526, "explanation": "Research data as provided by project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-13" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41479, "uuid": "9ff8f500e9624a6cb788249e2c3f25e5", "short_code": "acq", "title": "Acquisition for reaction products identified from toluene/m-xylene oxidation using GCxGC TOFMS at the University of Birmingham", "abstract": "Chamber reaction products identified from toluene/m-xylene oxidation using GCxGC TOFMS at the University of Birmingham" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2522, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24808, "uuid": "7ed9d8a288814b8b85433b0d3fec0300", "short_code": "proj", "title": "Atmospheric Pollution & Human Health in a Developing Megacity (APHH)", "abstract": "The Atmospheric Pollution & Human Health in a Developing Megacity (APHH) programme has two separate streams of activity looking at urban air pollution and its impact on Health in Chinese and Indian Megacities. The programme is a collaboration between NERC, the Medical Research Council (MRC) in the UK and the National Natural Science Foundation of China (NSFC) in China, and the Ministry of Earth Sciences (MoES) and Department of Biotechnology (DBT) in India." }, { "ob_id": 41476, "uuid": "a35bdd6fff1544e8826f5356960af09e", "short_code": "proj", "title": "Quantitative Attribution of Secondary Organic Aerosol in Beijing to its Precursors", "abstract": "Breathing particles in polluted air leads to the worsening of many health conditions and ultimately to premature death. The atmosphere of Beijing is well known for its very high concentrations of airborne particles and there is an urgent need for further control measures. A large proportion of those particles (referred to as \"haze\") are not emitted directly into the atmosphere but form within the atmosphere from chemical reactions of gases and vapours. This project is concerned with finding out which gases and vapours emitted into the atmosphere from road traffic, fuel burning, refuse incineration and many other sources are responsible for the formation of particles within the atmosphere of Beijing and the amount which they contribute to the concentration of particles. The scientific approach is to fill a reaction chamber with the vapours of a single chemical compound and let them undergo chemical reactions which lead to particle formation. The particles formed are then subject to very detailed chemical analysis and constituent molecules are identified which are characteristic of the compounds originally put into the chamber. Then, by making measurements of the same compound in the atmosphere, it is possible to estimate how much of the particles arise from the reaction of a particular gas. Control policies can then be formulated to reduce the emissions of those gases most responsible for particle formation. NERC ref NE/S006699/1." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 79912, 79913, 79914, 79915, 79916, 79917 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24817, "uuid": "648246d2bdc7460b8159a8f9daee7844", "short_code": "coll", "title": "APHH: Atmospheric measurements and model results for the Atmospheric Pollution & Human Health in a Chinese Megacity", "abstract": "The Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) Programme includes several projects making groundbased observations of meteorology, atmospheric chemical species and particulates in and around the city of Beijing. Due to the close working and exchange between the projects and overlap of instruments, this dataset collection contains measurements and related modelling study output produced by all these projects." } ], "responsiblepartyinfo_set": [ 202153, 202154, 202155, 202156, 202157, 202158, 202159, 202161, 202160 ], "onlineresource_set": [] }, { "ob_id": 41499, "uuid": "43eeb6248d7f435da2300edb34cc4bea", "title": "Daily Colour and Intensity Georeferenced Orthophotos of the Cliff and Beach at Happisburgh, Norfolk, UK (April-December 2019) version 2.", "abstract": "This dataset contains georeferenced orthophotos collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). The dataset contains 190 colour images and 190 intensity images in GEOTIFF format. The orthophotos are produced by projection of LiDAR (Light Detection And Ranging) scans of the coastal stretch. There are 190 images out of a possible 262 days, since only days when scans were performed from two locations are included, which did not happen every day due to weather conditions. The orthophotos are point-cloud renders of the scan data created using ScanLAB's proprietary point-cloud rendering engine. The colour orthophotos are rendered using the colour information projected onto the scan during post-process colourisation. The intensity orthophotos are rendered using the intensity data for each scan. The orthophotos are rendered using an orthographic virtual camera which frames the useful extents of the scan data and is orientated such that the rendered orthophoto is \"north-up\". The orthophotos are georeferenced using pythons GDAL library and ground-truth GPS measurements taken at the two TLS positions onsite. The procedure for this follows; 1) the ground-truth GPS positions were converted from OSGB 1936 / British National Grid to Latitude and Longitude., 2) The pixel resolution of the orthophotos was calculated from the pixel position of the two TLS in the orthophotos and the two ground truth GPS positions., 3) The GPS position of the top left pixel of the orthophoto was calculated using pixel position of a TLS and the pixel resolution., 4) A python script using the GDAL library applied the GPS metadata to the orthophoto and saved it in GEOTIFF format. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019). The files were re-submitted and this DOI represents the second version of the data.", "creationDate": "2024-02-23T13:53:32.836299", "lastUpdatedDate": "2024-02-23T13:53:32", "latestDataUpdateTime": "2024-02-23T13:53:32", "updateFrequency": "", "dataLineage": "The data are archived on the British Oceanographic Data Centre (BODC)'s space at the Centre for Environmental Data Analysis (CEDA) and assigned a DOI. No quality control procedures were applied by BODC.", "removedDataReason": "", "keywords": "", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-02-16T14:45:28", "doiPublishedTime": "2024-02-16T14:45:28", "removedDataTime": null, "geographicExtent": { "ob_id": 4153, "bboxName": "", "eastBoundLongitude": 1.54, "westBoundLongitude": 1.531, "southBoundLatitude": 52.823, "northBoundLatitude": 52.829 }, "verticalExtent": null, "result_field": { "ob_id": 41498, "dataPath": "/bodc/bgs240021", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 897, "numberOfFiles": 1, "fileFormat": "geotiff" }, "timePeriod": { "ob_id": 11571, "startTime": "2019-04-06T00:00:00", "endTime": "2019-12-23T23:59:59" }, "resultQuality": { "ob_id": 4535, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "BODC Data Quality Statement", "date": "2024-02-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12829 ], "observationcollection_set": [ { "ob_id": 40060, "uuid": "2c6f3201f01d4346a97ff8f08a8c15c9", "short_code": "coll", "title": "LiDAR (Light Detection And Ranging) images and model output from cliffs at Happisburgh, Norfolk, UK, 2019, from BLUE-coast and ScanLAB projects.", "abstract": "A colour LiDAR (Light Detection And Ranging) dataset was obtained at the cliffs at Happisburgh, Norfolk, UK, over a period of 9 months (April 6, 2019 to December 23, 2019). The scans were taken daily for 90% of the study period using a FARO S350 TLS (Terrestrial LiDAR Scanner). Scans were carried out from two locations consecutively, positioned at around 40 m from the cliffs. The full scans are also split into smaller subsets: \"slices\", 1 m wide bands oriented perpendicular to the shoreline, and \"grids\", smaller areas of the beach, to assist analysis. The numerical model SWAN (Simulated Waves Nearshore) (v41.31a), run in non-stationary mode, was used to simulate hourly sea states at the study site to aid in the context of environmental conditions. Wind parameters from the ERA5 reanalysis and bathymetry from the OceanWise 1 arc second digital elevation model (DEM) were used to force the SWAN model, and obtained wave parameters in 4x6 km rectangular grid around the scanning site, with a 10m interval, and a 26x26 km square grid encompassing the smaller grid, with a 100 m interval. The LiDAR scans were also projected into both colour and intensity images, viewing the shoreline from above. This research was funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project). The on-location LiDAR Scanning and Technical R&D operated by ScanLAB Projects Ltd was funded by Innovate UK's Audience of the Future Program (Multiscale 3D Scanning with Framerate for TV and Immersive Applications project). The first 6 months of LiDAR scans (April to September 2019) were funded by Innovate UK, and this project was continued by the NERC BLUE-coast funding for the last 3 months (October to December 2019)." } ], "responsiblepartyinfo_set": [ 202222, 202221, 202220, 202219, 202225, 202226, 202227, 202224, 202223, 202229, 202228, 202230, 202231, 202232, 202233, 202234, 202235 ], "onlineresource_set": [] }, { "ob_id": 41500, "uuid": "e652f0109f21401680bc3c0ac834a96e", "title": "Hydro-JULES: Global high-resolution drought datasets from 1981-2022 - Amendment to CHIRPS_GLEAM subset", "abstract": "This is a revised version of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) Global Land Evaporation Amsterdam Model (GLEAM) data from the Hydro-JULES: Global high-resolution drought dataset (doi:10.5285/ac43da11867243a1bb414e1637802dec).\r\n\r\nThis version corrects some errors found in the previous version due to the model run errors in some areas. The model has been re-run to accurately produce the dataset. \r\n\r\nThese are global scale high-resolution drought indices developed from a combination of precipitation and potential evapotranspiration datasets for the Hydro-JULES project. Climate Hazards group InfraRed Precipitation with Station data (CHIRPS), Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation estimates, Global Land Evaporation Amsterdam Model (GLEAM) and Bristol Hourly potential evapotranspiration (hPET) estimates were used. The drought index is developed using the Standardized Precipitation-Evapotranspiration Index (SPEI). These high-resolution global scale drought indices are available from 1981-2022 at a monthly and 5km spatial resolution. The SPEI indices are available from 1-48 months. The datasets provide valuable information for the study and analysis of droughts at much higher resolution from global to local scale. \r\n\r\nThese data were produced for Hydro-Jules (NE/S017380/1) and REACH (Foreign, Commonwealth and Development Office): Programme Code 201880.", "creationDate": "2023-05-22T15:03:02.061547", "lastUpdatedDate": "2023-05-22T15:03:02", "latestDataUpdateTime": "2024-05-30T16:41:30", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using Standardized Precipitation-Evapotranspiration Index (SPEI) at 5km horizontal resolution over the domain 180W-180E, 55S-85N. The dataset is available in NetCDF format. Data were produced by the project team before uploading to CEDA.\r\nThis data is a replacement version of the Climate Hazards group InfraRed Precipitation with Station data (CHIRPS) Global Land Evaporation Amsterdam Model (GLEAM) section of the Hydro-JULES: Global high-resolution drought datasets from 1981-2022 (https://catalogue.ceda.ac.uk/uuid/ac43da11867243a1bb414e1637802dec/) following the post-publication identification of an error in the model run.", "removedDataReason": "", "keywords": "NE/S017380/1, drought", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-03-21T13:41:15", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3830, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -55.0, "northBoundLatitude": 85.0 }, "verticalExtent": null, "result_field": { "ob_id": 41501, "dataPath": "/badc/hydro-jules/data/CHIRP-GLEAMS-amendment-2024/CHIRPS_GLEAM/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 358190874903, "numberOfFiles": 98, "fileFormat": "NetCDF" }, "timePeriod": { "ob_id": 11096, "startTime": "1981-01-01T00:00:00", "endTime": "2022-12-31T23:59:59" }, "resultQuality": { "ob_id": 4280, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-05-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40084, "uuid": "c4cb80ae80cd45d492ae8c27f606d878", "short_code": "comp", "title": "Computation for Hydro-JULES: Global Drought Indices", "abstract": "Data were generated using Standardized Precipitation-Evapotranspiration Index (SPEI) at 5km horizontal resolution over the domain 180W-180E, 55S-85N" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40079, "uuid": "9f364df79edc4aa69735bfaec25b1c07", "short_code": "proj", "title": "Hydro-JULES: Next generation land surface and hydrological prediction", "abstract": "Hydro-JULES is a NERC-funded research programme which will build a three-dimensional community model of the terrestrial water cycle to underpin hydrological research in the United Kingdom. Hydro-JULES is delivered by UKCEH in partnership with BGS and NCAS.\r\n\r\nThe Hydro-JULES model and its associated datasets will enable the UK to tackle outstanding research questions in hydrological science and will provide a national resource to support research both specific to the Hydro-JULES project and beyond.\r\n\r\nHydro-JULES will support and enable collaborative work across the research and academic community to:\r\naddress important science questions in the fields of hydrology, land-atmosphere feedbacks, carbon and nutrient cycles, data science and integration with novel instrumentation and Earth observation technologies;\r\nquantify the risks of hydro-climatic extremes (e.g., floods and drought) in a changing environment to support long-range planning and policy decisions;\r\nimprove hydrological forecasting using new sensors and modelling technology.\r\nThe Hydro-JULES project covers topics in land-surface science and hydrology including: quantification of hydro-meteorological risks, using high-resolution climate predictions for hydrological applications, calculation the impacts of environmental change on evaporation, transpiration, and soil moisture, modelling flood inundation over large areas, representing anthropogenic interventions in the water cycle, and application of new techniques including Earth observation and data assimilation. NE/S017380/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 63011, 63233 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 202237, 202236, 202243, 202238, 202239, 202240, 202241, 202242, 202244, 202245, 202246, 202247, 202248, 202249, 202250, 202251, 202252 ], "onlineresource_set": [] } ] }