Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=9200
{ "count": 10256, "next": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=9300", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/observations/?format=api&limit=100&offset=9100", "results": [ { "ob_id": 40799, "uuid": "bbdcf864f3af407cb651d2b2b84e077e", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from ZAMG's vaisala-cl51 instrument deployed at Altmunster, Austria", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from Zentralanstalt für Meteorologie und Geodynamik (ZAMG)'s vaisala-cl51 deployed at Altmunster, Austria.\n\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\n\nThe site has a corresponding WMO Integrated Global Observing System (WIGOS) id: 0-20000-0-11254.\n See online documentation for link to station details in the Observing Systems Capability Analysis and Review (OSCAR) Tool.\n \nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.", "creationDate": "2023-09-29T11:23:13.245413", "lastUpdatedDate": "2023-09-29T11:23:13.245424", "latestDataUpdateTime": "2025-07-18T00:52:17", "updateFrequency": "daily", "dataLineage": "Data were collected by instrument and transmitted to the central E-PROFILE processing hub at the UK's Met Office before preparation and delivery to the Centre for Environmental Data Analysis (CEDA). CEDA then produces daily concatenated files before ingestion into the CEDA Archive.", "removedDataReason": "", "keywords": "E-PROFILE, ceilometer measurements", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-09-29T11:20:12", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2989, "bboxName": "Altmunster", "eastBoundLongitude": 13.773329734802246, "westBoundLongitude": 13.773329734802246, "southBoundLatitude": 47.87916946411133, "northBoundLatitude": 47.87916946411133 }, "verticalExtent": null, "result_field": { "ob_id": 40798, "dataPath": "/badc/eprofile/data/daily_files/austria/altmunster/zmag-vaisala-cl51_A", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2347814135, "numberOfFiles": 913, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 11333, "startTime": "2021-03-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3890, "explanation": "The data are provided as-is with no quality control undertaken by the Centre for Environmental Data Analysis (CEDA). The data suppliers have not indicated if any quality control has been undertaken on these data.", "passesTest": true, "resultTitle": "E-PROFILE QC statement", "date": "2022-02-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 40800, "uuid": "48e2a4ccb25d4f59a3a4c42165e1bada", "short_code": "acq", "title": "ZAMG: vaisala-cl51 instrument deployed at Altmunster", "abstract": "vaisala-cl51 instrument instrument deployed at Altmunster operated by Zentralanstalt für Meteorologie und Geodynamik (ZAMG) providing cloud base height and aerosol profile data." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 220 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2527, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 7, "licenceURL": "https://artefacts.ceda.ac.uk/licences/cuncgl", "licenceClassifications": [ { "ob_id": 6, "classification": "personal" }, { "ob_id": 4, "classification": "academic" }, { "ob_id": 5, "classification": "policy" } ] } } ], "projects": [ { "ob_id": 32779, "uuid": "16a48b5339ab48cd97bb680388c5cddf", "short_code": "proj", "title": "EUMETNET E-PROFILE", "abstract": "E-PROFILE is part of the EUMETNET Composite Observing System, EUCOS, managing the European networks of radar wind profilers (RWP) and automatic lidars and ceilometers (ALC) for the monitoring of vertical profiles of wind and aerosols including volcanic ash.\r\n \r\n\r\nE-PROFILE coordinates the measurements of vertical profiles of wind from radar wind profilers (vertically pointing Doppler radars) and weather radars from a network of locations across Europe and provides the data to the end users. The main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users.\r\nDue to technical advances of the last years ceilometers (automatic low cost lidars) provide nowadays not only cloud base height but also information on the vertical distribution of aerosols derived from the backscatter profile. To make available this new observation capacity E-PROFILE is developing a framework to produce and exchange profiles of attenuated backscatter profiles. Automatic lidars and ceilometers of stations across Europe are added to the operational network." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50358, 50359, 50360, 50361, 50362, 50363, 50365, 50366, 50367, 50368, 50370, 50371, 50372, 50373, 62345, 62346, 62347 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 34905, "uuid": "345c47d378b64c75b7957aef0c09c81f", "short_code": "coll", "title": "EUMETNET E-PROFILE: ceilometer cloud base height and aerosol profile data from a network covering most of Europe with additional sites worldwide", "abstract": "Daily concatenated files of ceilometer cloud base height and aerosol profile data from a network of instruments within EUMETNET's E-PROFILE ALC network.\r\n\r\nThese data were produced by the EUMETNET's E-PROFILE processing hub as part of the ceilometer and lidar network operated as part of the by EUMETNET members. This network covers most of Europe with additional sites worldwide.\r\n\r\nMost datasets are available to registered CEDA users. For those not available to CEDA users application for access to those datasets under restricted access can be made using the links on one of the associated records. All use is made in accordance with the Closed-Use Non-Commercial General Licence. See datasets for further licencing links and for individual dataset citations.\r\n\r\nEUMETNET is a grouping of 31 European National Meteorological Services that provides a framework to organise co-operative programmes between its Members in the various fields of basic meteorological activities. One such programme is the EUMETNET Profiling Programme: E-PROFILE. See EUMETNET page linked from this record for further details of EUMETNET's activities.\r\n\r\nNote - the datasets listed on this collection are daily concatenated files produced from single time-step files for each instrument. CEDA holds an older archive of single time-step files (not linked to from the datasets or this collection) which will be aggregated together over time to extend these datasets further back to the start of the E-PROFILE holdings in the CEDA archives. Access to the older single time-step files ahead of their concatenation into daily files can be made via : https://data.ceda.ac.uk/badc/eprofile/data/. As these data are processed single time-step files will be removed from the archive.\r\n\r\nIt is not possible to support any requests for data that predates the CEDA holdings nor to back-fill any data gaps." } ], "responsiblepartyinfo_set": [ 198452, 198453, 198454, 198455, 198456, 198457, 198458, 198459, 198460 ], "onlineresource_set": [ 84961, 84962 ] }, { "ob_id": 40803, "uuid": "37a05772d207403fb3535695417c0fce", "title": "POLCOMS model run in the Atlantic margin of the Northwest European continental shelf generating potential temperature, salinity, and sea surface height for 41 years from 1964.", "abstract": "The dataset consists of temperature, salinity, and sea surface height data. The dataset is a gridded dataset covering the entire Northwest European continental shelf, extending out into the Atlantic Ocean and includes 40 depth layers. The grid resolution varies from 7.8 km to 14.2 km along the longitudinal axis and is equal to 12.3 km on the latitudinal axis. The data are daily mean values, calculated by averaging over a 25 hour tidal cycle, and cover the period from 01 January 1964 to 31 December 2004. They were produced during a study looking at mult-decadal variability and trends in temperature of the Northwest European continental shelf. The dataset was generated by the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) numerical model run by the National Oceanography Centre (NOC) Liverpool as part of Natural Environment Research Council (NERC) National Capability (NC) funding. The model simulations were run on the HECTOR supercomputer managed by the University of Edinburgh. The dataset consists of 41 data files in Climate and Forecast (CF) compliant NetCDF format.", "creationDate": "2023-09-29T11:23:27.578270", "lastUpdatedDate": "2023-09-29T11:23:27", "latestDataUpdateTime": "2023-09-12T16:36:52", "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": "final", "dataPublishedTime": "2023-09-29T09:32:39", "doiPublishedTime": "2023-09-29T09:32:39", "removedDataTime": null, "geographicExtent": { "ob_id": 3981, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.83333, "southBoundLatitude": 40.11111, "northBoundLatitude": 64.88889 }, "verticalExtent": null, "result_field": { "ob_id": 40802, "dataPath": "/bodc/POL130189/POLCOMS_DAILY_TS", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 215497140120, "numberOfFiles": 43, "fileFormat": "Climate Forecast NetCDFs, text" }, "timePeriod": { "ob_id": 11334, "startTime": "1964-01-01T00:00:00", "endTime": "2004-12-31T23:59:59" }, "resultQuality": { "ob_id": 4418, "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": "2023-09-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2569, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 36, "licenceURL": "https://www.bodc.ac.uk/data/documents/nodb/599476/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50577, 54763, 54764, 60438, 75631, 75634, 75635, 75636 ], "vocabularyKeywords": [], "identifier_set": [ 12705 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198469, 198468, 198467, 198466, 198472, 198473, 198474, 198471, 198470, 198475, 198476 ], "onlineresource_set": [] }, { "ob_id": 40805, "uuid": "3c156e5168844af1ac6572f692dda622", "title": "POLCOMS model run in the Atlantic margin of the Northwest European continental shelf generating eastward and northward baroclinic and barotropic current vectors for 41 years from 1964.", "abstract": "The dataset consists of eastward and northward baroclinic and barotropic current vectors. The dataset is a gridded dataset covering the entire Northwest European continental shelf, extending out into the Atlantic Ocean and includes 40 depth layers. The grid resolution varies from 7.8 km to 14.2 km along the longitudinal axis and is equal to 12.3 km on the latitudinal axis. The data are daily mean averages, calculated by averaging over a 25 hour tidal cycle, and cover the period from 01 January 1964 to 31 December 2004. They were produced during a study looking at mult-decadal variability and trends in temperature of the Northwest European continental shelf. The dataset was generated by the Proudman Oceanographic Laboratory Coastal Ocean Modelling System (POLCOMS) numerical model run by the National Oceanography Centre (NOC) Liverpool as part of Natural Environment Research Council (NERC) National Capability (NC) funding. The model simulations were run on the HECTOR supercomputer managed by the University of Edinburgh. The dataset consists of 41 data files in Climate and Forecast (CF) compliant NetCDF format.", "creationDate": "2023-09-29T11:44:22.282107", "lastUpdatedDate": "2023-09-29T11:44:22", "latestDataUpdateTime": "2025-01-18T03:20:18", "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": "final", "dataPublishedTime": "2023-09-28T16:56:10", "doiPublishedTime": "2023-09-28T16:56:10", "removedDataTime": null, "geographicExtent": { "ob_id": 3982, "bboxName": "", "eastBoundLongitude": 12.91667, "westBoundLongitude": -19.91667, "southBoundLatitude": 40.05556, "northBoundLatitude": 64.83334 }, "verticalExtent": null, "result_field": { "ob_id": 40804, "dataPath": "/bodc/POL130189/POLCOMS_DAILY_UV", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 218154036941, "numberOfFiles": 43, "fileFormat": "Climate Forecast NetCDF, text" }, "timePeriod": { "ob_id": 11334, "startTime": "1964-01-01T00:00:00", "endTime": "2004-12-31T23:59:59" }, "resultQuality": { "ob_id": 4419, "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": "2023-09-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [ { "ob_id": 2569, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 36, "licenceURL": "https://www.bodc.ac.uk/data/documents/nodb/599476/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50577, 54763, 54764, 60438, 75629, 75630, 75631, 75632, 75633 ], "vocabularyKeywords": [], "identifier_set": [ 12706 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198480, 198479, 198478, 198477, 198482, 198481, 198483, 198484, 198485, 198486, 198487 ], "onlineresource_set": [] }, { "ob_id": 40807, "uuid": "34e4bfe402c048c783e64eac0f0bca37", "title": "ESA Vegetation Parameters Climate Change Initiative (Vegetation_Parameters_cci): LAI and fAPAR, Version 1.0", "abstract": "Climate Research Data Package 1 from the ESA Climate Change Initiative Vegetation Parameters Project (Vegetation_parameters_cci). The dataset consists of Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) gridded at 1 km resolution for the period 2000-2020. The dataset is based on data from SPOT4/5-VEGETATION1/2 and PROBA-V as input data.\r\n\r\nLAI and fAPAR are retrieved using OptiSAIL (see Blessing and Giering, 2021 doi:10.20944/preprints202109.0147.v1). The dataset is processed for a north-south transect from Finland to South-Africa, as well as for a set of globally distributed sites that is representative for all biomes and for those sites where in-situ reference data is available.\r\n\r\nThe temporal resolution of both datasets is 5 days, but is computed using data selected from a symmetric 10-days window. The data are not smoothed in time. The transect is ordered in tiles following the PROBA-V tiling definition. These files contain the fully validated layers of (effective) LAI, fAPAR, their uncertainties and the correlation between both. The sites additionally include the variables Chlorophyll a+b leaf pigment concentration (Cab), the fraction of Chlorophyll Absorbed Photosynthetically Active Radiation (fAPAR_Cab) and Surface Albedo calculated as bi-hemispheric reflectance (BHR) for diffuse illumination with a reference spectrum for spectral broadband intervals visible wavelengths (VIS, 400-700 nm), near-infrared wavelengths (NIR, 700-2500 nm), and for the combined shortwave range (SW, 400-2500 nm), as well as directional-hemispherical reflectance (DHR) for the same spectral broadbands, computed for local solar noon. These additional variables are not validated.\r\n\r\nFurther details about the data, including validation and intercomparison with similar datasets, can be found in the PDF documentation.", "creationDate": "2023-09-29T15:57:45.503019", "lastUpdatedDate": "2023-12-18T16:25:43", "latestDataUpdateTime": "2025-01-10T01:54:43", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Vegetation Parameters CCI team as part of the ESA Climate Change Initiative (CCI) and is being held on the CEDA (Centre for Environmental Data Analysis) archive as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "ESA, CCI, Vegetation, LAI, fAPAR, climate change, GCOS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-12-18T16:26:50", "doiPublishedTime": "2023-12-18T16:36:43", "removedDataTime": null, "geographicExtent": { "ob_id": 3994, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -57.0, "northBoundLatitude": 75.0 }, "verticalExtent": null, "result_field": { "ob_id": 40990, "dataPath": "/neodc/esacci/vegetation_parameters/data/L3S/vp_products/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 282485273505, "numberOfFiles": 54006, "fileFormat": "The data are provided in netCDF format." }, "timePeriod": { "ob_id": 11354, "startTime": "2000-01-01T00:00:00", "endTime": "2020-06-30T23:59:59" }, "resultQuality": { "ob_id": 4468, "explanation": "See the Vegetation Parameters CCI documentation for information on data quality.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-12-14" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41219, "uuid": "2f4db06ad5aa4c53bd6a8529b3ca9daa", "short_code": "cmppr", "title": "Composite Process for: ESA Climate Change Initiative Vegetation Parameters LAI and fAPAR v1 data", "abstract": "Leaf Area Index (LAI) and fraction of Absorbed Photosynthetically Active Radiation (fAPAR) are retrieved from SPOT4/5-VEGETATION1/2 and PROBA-V data using the OptiSAIL algorithm (see Blessing and Giering, 2021 doi:10.20944/preprints202109.0147.v1)." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2607, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 66, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_vegetation_parameters_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40992, "uuid": "efbb5cb1aef14504a0432cfe9b4e3d58", "short_code": "proj", "title": "ESA Vegetation Parameters Climate Change Initiative Project", "abstract": "The European Space Agency Vegetation Parameters Climate Change Initiative (Vegetation_Parameters_cci) project is part of the ESA Climate Change Initiative (CCI) programme, which aims to produce datasets of Essential Climate Variables (ECV's) from satellite datasets." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 50559, 50561, 62501, 67723, 74293, 74294, 74295, 74296, 74297, 74298, 74299, 74300, 74301, 74302, 74303, 74304, 74305, 74306, 74307, 74308, 74309, 74310, 74311, 74312, 74313, 74314, 74315, 74316, 74317, 74318, 74319, 74320, 74321, 74322, 74323, 74324, 74325, 74326, 74327, 74328, 74329, 74330, 74331, 74332, 74333, 74334, 74335, 74336, 74337, 74338, 74339, 74340, 74341, 74342, 74343, 74344, 74345, 74346, 74347, 74348, 74349, 74350, 74351, 74352, 74353, 74354, 74355, 74356, 74357, 74358, 74359, 74360 ], "vocabularyKeywords": [], "identifier_set": [ 12792 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198499, 198500, 198501, 198502, 198503, 198504, 198690, 198691, 198692, 198693, 198694, 198695, 198696, 198697, 198698, 198699, 198700, 198701 ], "onlineresource_set": [ 85401, 85404, 85405, 85406, 85407, 85408, 85409, 87953 ] }, { "ob_id": 40841, "uuid": "86d4b9195b40469e920cb56044adb265", "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 3", "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 3 of this dataset which corrects an error in the implementation of the correction\r\nof the lidar azimuth when the scanning head slipped at very low temperatures.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-09-29T14:29:43", "latestDataUpdateTime": "2024-09-11T11:59:59", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the instrument scientists before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOSAiC, NE/S002472/1, AMOF,NCAS", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-10-17T13:19:12", "doiPublishedTime": "2023-10-17T13:19:34.360722", "removedDataTime": null, "geographicExtent": { "ob_id": 3419, "bboxName": "", "eastBoundLongitude": 148.38, "westBoundLongitude": -165.07, "southBoundLatitude": 78.34, "northBoundLatitude": 89.99 }, "verticalExtent": null, "result_field": { "ob_id": 42759, "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1573434427, "numberOfFiles": 593, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 10877, "startTime": "2019-10-05T00:00:00", "endTime": "2020-09-20T00:00:00" }, "resultQuality": { "ob_id": 3908, "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": "2022-03-24" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 37048, "uuid": "ce2aa0607afc45c1a24abd5ddee091f0", "short_code": "acq", "title": "Acquisition for: Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC): Wind profiles from Galion G4000 Lidar Wind Profiler", "abstract": "Acquisition for: Multidisciplinary drifting Observatory for Study of Arctic Climate (MOSAiC): Wind profiles from Galion G4000 Lidar Wind Profiler" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 223 ], "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": 37021, "uuid": "35a5a43ae2fa4289af0a3e5e2ca92a5a", "short_code": "proj", "title": "MOSAiC:The Multidisciplinary drifting Observatory for the Study of Arctic Climate", "abstract": "The Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) initiative was a major international programme motivated by the rapid changes in Arctic climate observed over the last few decades. This is driven by an accelerated rise in the mean temperature of the Arctic; which is warming at 2-3 times the mean global rate. The most visible change is the dramatic reduction in sea ice extent, particularly of the summer minimum, which is decreasing at a rate of 13% per decade.\r\n\r\nThese rapid changes are the result of a combination of feedback processes - the best known is the ice albedo feedback, whereby the loss of ice exposes the land or sea surface beneath, lowering the area mean albedo and allowing more solar radiation to be absorbed, which warms the surface and enhances ice melt. Other feedbacks relate to the vertical profiles of atmospheric temperature and humidity, cloud properties, and large-scale atmospheric circulation.\r\n\r\nWhile climate models also show enhanced warming in the Arctic, they do not reproduce many of the observed details of the change; for example they do not reproduce the very rapid decline in the summer sea ice minimum observed over the last 10 years, and there are big differences between models. This has a significant impact on our ability to predict the future state of climate system. Poor model performance results from multiple leading-order deficiencies in their representation of physical processes in the Arctic system. MOSAiC aims to address these through a large-scale coordinated approach, making simultaneous measurements of the many interdependent processes relevant to climate over a full calendar year. This approach is necessary because of the strong linkages and feedbacks between different parts of the Arctic climate system and the strong seasonality in many processes. \r\nThe MOSAiC Boundary layer is a Natural Environment Research Council (NERC, grant: NE/S002472/1) funded contribution to this international project focused on measurements of atmospheric boundary layer dynamics and turbulent structure. This observational campaign took place on, and around, the icebreaker Polarstern, which was frozen in at the edge of the pack ice at the end of the summer melt. This provided ready access to both multi-year ice within the pack and to freshly forming ice just outside it. Measurements were made of all components of the surface energy budget on both the upper and lower sides of the ice, along with ice thickness, temperature, physical properties, topography, and deformation over time. The processes controlling the energy budget, including synoptic-scale forcing, cloud properties, turbulent mixing, and the interactions between them, will be studied in detail.\r\n\r\nGrantRef: NE/S002472/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 56577, 62763, 62764, 62765, 62766, 62767, 62768, 62769, 62770, 62771, 62772, 62773, 62774, 62775, 62776, 62777, 75628 ], "vocabularyKeywords": [], "identifier_set": [ 12722 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198614, 198615, 198616, 198617, 198618, 198619, 198620, 198621 ], "onlineresource_set": [] }, { "ob_id": 40843, "uuid": "7fc9df8070d34cacab8092e45ef276f1", "title": "ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 2.1", "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.1.0 of the dataset.\r\n\r\nThe six thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n• Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.\r\n\r\nSatellite sensors associated with the thematic climate variables are as follows:\r\nLWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; \r\nLWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;\r\nLSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; \r\nLIC: Terra/Aqua MODIS; \r\nLWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;\r\nLIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website and in Carrea, L., Crétaux, JF., Liu, X. et al. 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A range of scientific disciplines including hydrology, limnology, climatology, biogeochemistry and geodesy are interested in distribution and functioning of the millions of lakes (from small ponds to inland seas), from the local to the global scale. Remote sensing provides an opportunity to extend the spatio-temporal scale of lake observation. In this context, the Lakes_cci develops products for the following five thematic climate variables:\r\n•\tLake Water Level (LWL): a proxy fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate changes.\r\n•\tLake Water Extent (LWE): a proxy for change in glacial regions (lake expansion) and drought in many arid environments, water extent relates to local climate for the cooling effect that water bodies provide.\r\n•\tLake Surface Water temperature (LSWT): correlated with regional air temperatures and a proxy for mixing regimes, driving biogeochemical cycling and seasonality. \r\n•\tLake Ice Cover (LIC): freeze-up in autumn and advancing break-up in spring are proxies for gradually changing climate patterns and seasonality. \r\n•\tLake Water-Leaving Reflectance (LWLR): a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n\r\nIn this context, Lakes_cci represents a unique framework to provide consistent and homogenous data to the multiple communities of lake scientists. The project actively engages with this community to assess the utility and future improvement of Lakes_cci products." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 8143, 8144, 12066, 50559, 50561, 63576, 68335, 68336, 68337, 68338, 68339, 68340, 68341, 68342, 68343, 68344, 68345, 68346, 68347, 68348, 68349, 68350, 68351, 68352, 68353, 68354, 68355, 68356, 68357, 68358, 68359, 68360, 68361, 68362, 68363, 84538, 84539, 84540, 84541, 84542, 84543, 84544, 84545, 84546, 84547, 84548, 84549, 84550, 84551, 84552, 84553, 84554, 84555, 84556, 84557, 84558, 84559, 84560, 84561, 84562, 84563, 84564, 84565, 84566, 84567, 84568, 84572, 84574, 84575, 84576, 84577, 84579, 84580, 84584, 84585, 84588, 84593, 84598, 84599, 84600, 84698, 84699, 84700, 84701, 84702, 84703, 84704, 84705 ], "vocabularyKeywords": [ { "ob_id": 10241, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_topexPoseidon", "resolvedTerm": "Topex/Poseidon" }, { "ob_id": 10184, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_envisat", "resolvedTerm": "Envisat" }, { "ob_id": 10493, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platformProg/plat_metop", "resolvedTerm": "Metop" }, { "ob_id": 10445, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_jason3", "resolvedTerm": "Jason-3" }, { "ob_id": 10905, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_saral", "resolvedTerm": "SARAL" }, { "ob_id": 10195, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_jason1", "resolvedTerm": "Jason-1" }, { "ob_id": 10333, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/product/prod_modTe", "resolvedTerm": "MODIS_TERRA" }, { "ob_id": 10968, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platformProg/plat_sentinel2_prog", "resolvedTerm": "Sentinel-2" }, { "ob_id": 10169, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_jason2", "resolvedTerm": "Jason-2" }, { "ob_id": 10332, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/product/prod_modAqu", "resolvedTerm": "MODIS_AQUA" }, { "ob_id": 10365, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platformProg/plat_ers", "resolvedTerm": "ERS" }, { "ob_id": 10912, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel3b", "resolvedTerm": "Sentinel-3B" }, { "ob_id": 10498, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_landsat_4", "resolvedTerm": "Landsat-4" }, { "ob_id": 10447, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_landsat_7", "resolvedTerm": "Landsat-7" }, { "ob_id": 10416, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_landsat_5", "resolvedTerm": "Landsat-5" }, { "ob_id": 10384, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/platform/plat_landsat_8", "resolvedTerm": "Landsat-8" }, { "ob_id": 10967, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platformProg/plat_sentinel1_prog", "resolvedTerm": "Sentinel-1" }, { "ob_id": 10911, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel3a", "resolvedTerm": "Sentinel-3A" } ], "identifier_set": [ 12856 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198629, 198622, 198623, 198624, 198625, 198626, 198627, 198628, 198630, 202190, 198631, 198632, 198633, 198634, 198635, 198636, 198637, 198638, 198639, 198640, 198641, 198642, 198643, 198644, 198645 ], "onlineresource_set": [ 85021, 85020, 85022, 85024, 85025 ] }, { "ob_id": 40844, "uuid": "47c849b907414f3ab5e56ba9cf83e779", "title": "Met Office Projecting Future Sea Level (ProFSea) tool, input datasets", "abstract": "The Met Office Projecting Future Sea Level (ProFSea) tool requires several input datasets, which are provided here and described below:\r\n\r\n1. Coupled Model Intercomparison Project Phase 5 (CMIP5) model timeseries of global thermosteric sea level (zostoga) and spatial fields of ocean dynamic sea level (zos) on a regular 1 x 1 latitude-longitude grid.\r\n\r\n2. UKCP18 CMIP5 slope coefficients calculated between local sterodynamic component of sea level (zostoga + zos) and global thermosteric sea level (zostoga).\r\n\r\n3. A 450,000-member Monte Carlo simulation is produced for each RCP scenario that forms the basis of both the global mean sea level and local mean sea level projections, and for the two different time horizons, to 2100 and 2300. Essentially a Monte Carlo can be defined as a method that makes random draws from an underlying distribution multiple times to build up a distribution of the combined uncertainties. The methods used for each component and for our two different time horizons are summarized in Table 1, Section 3 of Palmer et al. (2020).\r\n\r\n4a. UKCP18 estimates of the effect of glacial isostatic adjustment (GIA) on relative sea level change developed for the NERC BRITICE-CHRONO project (Sarah Bradley, pers. Comm.; UKCP18 Marine Report).\r\n\r\n4b. Estimates of the effect of GIA on relative sea level based on two of the three global estimates used in Palmer et al (2020). The first is based on the ICE‐5G (VM2 L90) model (Peltier, 2004). The other is from the Australian National University based on an update of Nakada and Lambeck (1988) in 2004-2005 (Slangen et al., 2014). These are gridded fields with global coverage.\r\n\r\n5. The Gravity, Rotation and Deformation (GRD) estimates for each of the barystatic components: (i) Antarctic surface mass balance, (ii) Antarctic ice dynamics, (iii) Greenland surface mass balance, (iv) Greenland ice dynamics, (v) worldwide glaciers, and (vi) changes in land water storage. For details of the different estimates see Section 2.4 of Palmer et al (2020). These are gridded fields with global coverage.", "creationDate": "2023-10-18T08:21:47.540517", "lastUpdatedDate": "2024-08-20T12:35:48", "latestDataUpdateTime": "2024-09-11T11:59:59", "updateFrequency": "asNeeded", "dataLineage": "Building on methodologies from IPCC AR5 and further developed in the UK Climate projections (2018) and the Strategic Priorities Fund UK Climate Resilience Programme, this data facilitates a globally relocatable sea level rise tool published by the Met Office. The “description” section contains the details for specific data products this tool incorporates. The scientific methods are based on the work of Palmer et al (2020), and very similar to those described in the UKCP18 Marine Report. Full details of the data sources and processing are available in Palmer et al (2020).", "removedDataReason": "", "keywords": "global thermosteric sea level,ocean dynamic sea level,glacial isostatic adjustment,gravity, rotation and deformation,sea level rise,climate projections", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-11-22T16:13:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3992, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40966, "dataPath": "/badc/deposited2023/profsea_tool", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15528942920, "numberOfFiles": 455, "fileFormat": "This dataset contains data in netCDF and pickle file format." }, "timePeriod": { "ob_id": 11352, "startTime": "2007-01-01T00:00:00", "endTime": "2100-12-31T23:59:59" }, "resultQuality": { "ob_id": 4431, "explanation": "Met Office Quality Assurance processes and standards have been applied to the development and documentation of this tool and its outputs.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-10-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 69 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6255, 9064, 13935, 13936, 50510, 50568, 50612, 50617, 56259, 57010, 63972, 74962, 75605, 75606, 75607, 75608, 75609, 75610, 75611, 75612, 75613, 75614, 75615, 75616, 75617, 75618, 75619, 75620, 75621, 75622, 75623, 75624, 75625, 75626, 75627 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198647, 198648, 198649, 198650, 198651, 198652, 198646, 198681 ], "onlineresource_set": [ 85028, 85029, 85031, 85030, 85255, 85256, 85257, 87929 ] }, { "ob_id": 40846, "uuid": "43be78ab26cc4eb4b08a4f47a0c2a0fa", "title": "Northwest European Shelf daily and monthly physical-biogeochemical data (1990-2099) from NEMO-ERSEM coupled physics-biogeochemistry climate model forced by GFDL-ESM2G under scenario RCP8.5.", "abstract": "This dataset consists of daily averages of temperature, salinity, river nitrogen inputs, velocity vectors, nutrient, oxygen, and plankton related carbon and chlorophyll a data as well as monthly averages of carbonate chemistry, including air-sea and pelagic-benthos fluxes, and plankton related carbon, nitrogen, respiration, and primary production data. This is a gridded dataset, covering the Northwest European continental shelf from 40 to 60 degrees latitude and from -19 to 13 degrees longitude, with a horizontal resolution of 7 km and 52 sigma depth levels corresponding to the Atlantic Margin Model 7 km (AMM7) domain. The data cover the period from 1990 to 2099. These outputs are downscaled projections, obtained by running the Nucleus for European Modelling of the Ocean (NEMO) and European Regional Seas Ecosystem Model (ERSEM) coupled physics-biogeochemistry climate model, forced with lateral and atmospheric boundary conditions from the Geophysical Fluid Dynamics Laboratory, GFDL-ESM2G, Earth System Model from the Coupled Model Intercomparison Project collection, CMIP5, running under the RCP8.5 emission scenario. This dataset was generated by the Plymouth Marine Laboratory (PML) and the National Oceanography Centre (NOC), under Natural Environment Research Council (NERC) grant Resolving Climate Impacts on shelf and CoastaL sea Ecosystems, ReCICLE (grant numbers NE/M004120/1 and NE/M003477/2). The simulations were run on the ARCHER supercomputer managed by the University of Edinburgh.", "creationDate": "2023-10-18T08:50:02.448093", "lastUpdatedDate": "2023-10-18T08:50:02", "latestDataUpdateTime": "2024-03-09T01:53:18", "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": "final", "dataPublishedTime": "2023-10-17T12:15:05", "doiPublishedTime": "2023-10-17T12:15:05", "removedDataTime": null, "geographicExtent": { "ob_id": 3993, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.0, "southBoundLatitude": 40.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": { "ob_id": 40845, "dataPath": "/bodc/PML230162/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9067956410900, "numberOfFiles": 7922, "fileFormat": "Climate Forecast NetCDF, text" }, "timePeriod": { "ob_id": 11351, "startTime": "1990-01-01T00:00:00", "endTime": "2099-12-31T23:59:59" }, "resultQuality": { "ob_id": 4432, "explanation": "The data are provided as-is with no quality control undertaken by the British Oceanographic Data Centre (BODC). 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This is a gridded dataset, covering the Northwest European continental shelf from 40 to 60 degrees latitude and from -19 to 13 degrees longitude, with a horizontal resolution of 7 km and 52 sigma depth levels corresponding to the Atlantic Margin Model 7 km (AMM7) domain. The data cover the period from 1990 to 2099. These outputs are downscaled projections, obtained by running the Nucleus for European Modelling of the Ocean (NEMO) and European Regional Seas Ecosystem Model (ERSEM) coupled physics-biogeochemistry climate model, forced with lateral and atmospheric boundary conditions from the Institut Pierre-Simon Laplace Coupled Model with mid-resolution, IPSL-CM5A-MR, from the Coupled Model Intercomparison Project collection, CMIP5, running under the RCP8.5 emission scenario. This dataset was generated by the Plymouth Marine Laboratory (PML) and the National Oceanography Centre (NOC), under Natural Environment Research Council (NERC) grant Resolving Climate Impacts on shelf and CoastaL sea Ecosystems, ReCICLE (grant numbers NE/M004120/1 and NE/M003477/2). The simulations were run on the ARCHER supercomputer managed by the University of Edinburgh.", "creationDate": "2023-10-18T08:50:31.660730", "lastUpdatedDate": "2023-10-18T08:50:31", "latestDataUpdateTime": "2023-10-09T09:36:47", "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": "final", "dataPublishedTime": "2023-10-17T12:14:58", "doiPublishedTime": "2023-10-17T12:14:58", "removedDataTime": null, "geographicExtent": { "ob_id": 3993, "bboxName": "", "eastBoundLongitude": 13.0, "westBoundLongitude": -19.0, "southBoundLatitude": 40.0, "northBoundLatitude": 60.0 }, "verticalExtent": null, "result_field": { "ob_id": 40847, "dataPath": "/bodc/PML230161/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9213338763846, "numberOfFiles": 7922, "fileFormat": "Climate Forecast NetCDF, text" }, "timePeriod": { "ob_id": 11351, "startTime": "1990-01-01T00:00:00", "endTime": "2099-12-31T23:59:59" }, "resultQuality": { "ob_id": 4433, "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": "2023-10-18" }, "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": [ 50577, 74437, 74438, 74439, 74440, 74441, 74442, 74443, 74444, 74445, 74446, 74447, 74448, 74449, 74450, 74451, 74452, 74453, 74454, 74455, 74456, 74457, 74458, 74459, 74460, 74461, 74462, 74463, 74464, 74465, 74466, 74467, 74468, 74469, 74470, 74471, 75583, 75584, 75585, 75586, 75587, 75588, 75589, 75590, 75591, 75592, 75593, 75594, 75595, 75596, 75597, 75598, 75599, 75600, 75601, 75602, 75603, 75604 ], "vocabularyKeywords": [], "identifier_set": [ 12724 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198670, 198669, 198668, 198667, 198673, 198674, 198675, 198672, 198671, 198676, 198677, 198678, 198679, 198680 ], "onlineresource_set": [] }, { "ob_id": 40850, "uuid": "1d01d0efb6c24c218489605b5aa44cf5", "title": "ESA RECCAP-2 Climate Change Initiative (RECCAP2_cci): Methane-Centric Wetland Dataset Based on GIEMS (1992-2020), v1.0", "abstract": "To aid methane emission modelling within ESA's Regional Carbon Cycle Assessment and Processes Phase 2 (RECCAP-2) project, a methane-centric wetland dataset based on the Global Inundation Estimate from Multiple Satellites (GIEMS-2) database has been produced.\r\n\r\nThe GIEMS-2 database provides the monthly extent of the continental water surfaces, including lakes, rivers, wetlands, and rice paddies, from 1992 to 2015, as described in Prigent et al. (2020). It is on a 0.25 x 0.25 degree regular grid in latitude and longitude. It has recently been extended to 2020 within the RECCAP-2 project.\r\n\r\nFor methane emission modeling, three water surface types are usually considered separately: the permanent water surfaces (such as lakes, rivers, and reservoirs), the rice paddies, and the wetlands (i.e., the remaining water surfaces). As a consequence, the possibility to separate these contributions within the GIEMS pixels is required. This methane-centric GIEMS dataset isolates wetlands from the other surface waters in order to facilitate the estimation of the wetland methane emissions.", "creationDate": "2023-10-18T15:39:12.515265", "lastUpdatedDate": "2023-10-18T15:22:25", "latestDataUpdateTime": "2023-07-27T17:15:46", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "RECCAP-2,giems,wetland,methane", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-12-18T16:42:20", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40849, "dataPath": "/neodc/esacci/reccap2/data/giems_methane_centric/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 54077094, "numberOfFiles": 2, "fileFormat": "The data are in netCDF format." }, "timePeriod": { "ob_id": 11353, "startTime": "1992-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 4469, "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": "2023-12-18" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1140, "name": "ESACCI" } ], "permissions": [ { "ob_id": 2590, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 53, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_reccap2_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 41107, "uuid": "727dea0ecf0f4d6ca0bf6459c37671bb", "short_code": "proj", "title": "ESA RECCAP-2 Climate Change Initiative (RECCAP2_cci)", "abstract": "The REgional Carbon Cycle Assessment and Processes Phase 2 (RECCAP-2) project is coordinated by the Global Carbon Project and has the following objectives:\r\n1.) To improve quantification of anthropogenic greenhouse gas emissions and their sources;\r\n2.) To develop robust observation-based estimates of changes in carbon storage and greenhouse gas emissions and sinks by the oceans and terrestrial ecosystems, distinguishing whenever possible anthropogenic versus natural fluxes and their driving processes;\r\n3.) To gain science-based evidence of the response of marine and terrestrial regional greenhouse gas budgets to climate change and direct anthropogenic drivers." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 62780, 62781, 75581, 75582 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 198682, 198683, 198684, 198685, 198686, 198687, 198688, 198689, 199760 ], "onlineresource_set": [ 85032, 85033, 85034, 85654 ] }, { "ob_id": 40851, "uuid": "325a4dde60d142049339e0c84816aac1", "title": "ForestScan Project: Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) and Terrestrial Laser Scanning (TLS) data of FBRMS-01: Paracou, French Guiana plot 6, 10th October to 15th November 2019", "abstract": "This dataset contains LiDAR scanning derived products (raw scanner data, geo-located point clouds, individual 3D tree models) collected over the north-eastern part (200 m x 200 m) of FBRMS-01: Paracou, French Guiana plot 6. The campaign took place from the 10th of October to the 15th of November 2019. Terrestrial LiDAR Scanning (TLS) was conducted on a regular grid with spacing of 10 m with a RIEGL VZ-400 scanner and retro-reflective targets for scan registration. Unpiloted Aerial Vehicle Laser Scanning (UAV-LS) was conducted with a RIEGL Ricopter with VUX-SYS VUX-1UAV system with varying flight heights and flight directions.\r\n\r\nThe TLS point clouds were collected to produce explicit 3D models of individual trees and subsequently estimate their above-ground biomass (AGB). The UAV-LS point clouds were collected to test scanner settings and inspect point clouds properties, in particular with regard to their suitability to model individual trees and their AGB.\r\n\r\nThe campaign was conducted by researchers Benjamin Brede, Harm Bartholomeus and Alvaro Lau of the Laboratory of Geo-Information Science and Remote Sensing of Wageningen University & Research (The Netherlands) with support from Nicolas Barbier of AMAP Lab (Botany and Modeling of Plant Architecture and Vegetation).", "creationDate": "2023-11-06T14:43:30.181776", "lastUpdatedDate": "2023-11-06T14:40:13", "latestDataUpdateTime": "2025-03-29T01:52:17", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). The UCL team provided revised metadata for the catalogue record", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-28T15:23:30", "doiPublishedTime": "2025-03-28T16:52:43.976092", "removedDataTime": null, "geographicExtent": { "ob_id": 3953, "bboxName": "UAV Paracou", "eastBoundLongitude": -52.032647, "westBoundLongitude": -52.9211533, "southBoundLatitude": 5.262807, "northBoundLatitude": 5.280352 }, "verticalExtent": null, "result_field": { "ob_id": 40852, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/TLSandUAV_ProductsandRaw_Paracou_2019", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 240803731311, "numberOfFiles": 7000, "fileFormat": "Raw and Point cloud" }, "timePeriod": { "ob_id": 12020, "startTime": "2019-10-10T00:00:00", "endTime": "2019-11-15T00:00:00" }, "resultQuality": { "ob_id": 4464, "explanation": "Data were validated by the Forestscan project team and provided to CEDA for archival.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-30" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43412, "uuid": "9a471ee07ff64728a78b0b2a8e0b099d", "short_code": "cmppr", "title": "UAV and TLS Paracou", "abstract": "WUR RIEGL VZ-400 Terrestrial LiDAR scanner TLS2trees and TreeQSM\"," }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13274 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198702, 198703, 198704, 198705, 198706, 198707, 200164, 200218, 200167, 200165, 206616, 200166, 206617, 206618 ], "onlineresource_set": [ 88332, 88333, 85463, 88337, 94259 ] }, { "ob_id": 40853, "uuid": "7bef89a9dc404683a46642625a024a4b", "title": "ForestScan: Aerial Laser Scanning (ALS) of FBRMS-01: Paracou, French Guiana, November 2022", "abstract": "This Aerial Laser Scanning (ALS) campaign was conducted in November 2022. The ALS data corresponding to plots FG5c1, FG6c2, FG8c4 and IRD-CNES also scanned by Terrestrial LiDAR Scanning (TLS) in October or November 2022 as part of the ForestScan Project are provided in four separate laz files.\r\n\r\nThe covered area: 3*2.16 ha + 1*1.44 ha; Pulse density: ~200 m2; Scanner type: VQ 780II RIEGL; Scanner wavelength: 1064 nm; Beam divergence: <=0.25 mrad (1/e2); Vehicle: Airplane BN2; Operator: Altoa. Acquisition parameters: swath angle: +/-20 degrees; PRR (channel type): ~ 1000 kHz; Ground footprint size of pulse: ~0.16 m; Flight height: 650m terrain follow mode (AGL); Acquisition mode: Full waveform, RGB camera on board but no orthomosaïc made.", "creationDate": "2023-11-06T15:15:44.823209", "lastUpdatedDate": "2023-11-06T15:11:38", "latestDataUpdateTime": "2023-08-02T17:16:12", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by Gregoire Vincent.", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Forest structure", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-28T14:45:28", "doiPublishedTime": "2025-03-28T16:52:41.441488", "removedDataTime": null, "geographicExtent": { "ob_id": 3953, "bboxName": "UAV Paracou", "eastBoundLongitude": -52.032647, "westBoundLongitude": -52.9211533, "southBoundLatitude": 5.262807, "northBoundLatitude": 5.280352 }, "verticalExtent": null, "result_field": { "ob_id": 43413, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/ALS-Paracou-2022-PerPlot", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 168157094, "numberOfFiles": 6, "fileFormat": "Storage format is las 1.2 (terrestrial laser scanner)" }, "timePeriod": { "ob_id": 12163, "startTime": "2022-11-23T00:00:00", "endTime": "2022-11-24T00:00:00" }, "resultQuality": { "ob_id": 4697, "explanation": "Data was processed (classified and tiled) by the project team. Noise points were also removed from data.", "passesTest": true, "resultTitle": "Data Quality Statement ALS ForestScan Paracou", "date": "2025-03-24" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 43710, "uuid": "276801a95b6942a8bd2e8d92c19f08c9", "short_code": "acq", "title": "ForestScan UAV Paracou 2022", "abstract": "The ForestScan UAV Paracou 2022Covered area: 3*2.16 ha + 1*1.44 ha; Pulse density: ~200 m2; Scanner type: VQ 780II RIEGL; Scanner wavelength: 1064 nm; Beam divergence: <=0.25 mrad (1/e2); Vehicle: Airplane BN2; Operator: Altoa. Acquisition parameters: swath angle: +/-20 degrees; PRR: ~ 1000 kHz; Ground footprint size of pulse: ~0.16 m; Flight height: 650m AGL; Acquisition mode: Full waveform, RGB camera on board but no orthomosaïc made (see data delivery report); Data delivery report of operator: available; Funding: ANR Labex CEBA (FRANCE)." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" }, { "ob_id": 43702, "uuid": "fa308f5639a54d0ba0b401c206042797", "short_code": "proj", "title": "Amazonian Landscapes in Transition", "abstract": "The Amazonian Landscapes in Transitions (ALT) project seeks to address the a fundamental question as to whether Amazonian forests will be able to withstand the simultaneous impacts of climate and local anthropogenic disturbances. This question is of global relevance, as France is directly involved in negotiations on the potential of its forests to both offset carbon emissions and mitigate the biodiversity crisis. \r\n\r\nThe project approach was to calibrate detailed forest dynamic models to generate maps of ecological indicators that will account for forest vulnerability, in addition to current forest indicators. Such territorial model-based estimates of ecological indicators for French Guiana will be associated with uncertainty maps. This strategy depends on the consolidation of a solid knowledge based of accurate forest inventories, including trees in the forest understory, which condition the future of forest regeneration, on information on plant functional diversity, and on patterns and processes of tree mortality." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13273 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198708, 198709, 198710, 198711, 198712, 198713, 209142, 209143 ], "onlineresource_set": [ 94260 ] }, { "ob_id": 40855, "uuid": "b1cd34f6af7941a3b1429ac52a3f6b28", "title": "ForestScan: Terrestrial Laser Scanning (TLS) of FBRMS-01: Paracou, French Guiana 1ha plot IRD-CNES, October 2021", "abstract": "Terrestrial laser scanning (TLS) was conducted in October 2021 by G. Vincent and J-L Smock (IRD) using a Riegl VZ-400. Scans were acquired at locations on a 10 m Cartesian grid. Capturing a complete sample of the scene at each location requires two scans (upright and tilted), owing to a 100° field of view. 249 scans in total were collected. The angular resolution between sequentially fired pulses was 0.04°, resulting in approximately 22.4 million emitted pulses per scan (i.e., 5.42 billion per ha). Up to four targets can be resolved per pulse, with a nominal ranging accuracy of 5 mm. The laser itself is characterised by a beam divergence of 0.35 mrad, and the diameter of the beam at emission is 7 mm (e.g., the diameter of the beam at a range of 50 m, would be 21 mm). The pulse repetition rate was 300 kHz, therefore, each scan took approximately 3 minutes to complete. To generate a plot-level point cloud from individual scans, all scans were co-registered and projected to a standard geographical coordinate system (epsg 2972). To this end, 5 identifiable targets with known X,Y,Z coordinates (plot corners + plot centre) were positioned using a total station.\r\n\r\nOnce co-registered using RiScanPro software, individual scans were exported in las extrabyte format (including deviation) using LidarFomartConverter v.1.2.(AMAP code based on RivLib). Reflectance range was set to -30dB to +5dB and stored in the Intensity field as a long integer. Echoes outside this reflectance range were discarded. Coordinate precisions were set to 0.001 m. The full point cloud (all 249 scans) was then cropped to 1.4 ha plot (+10m buffer around 100x100m plot), and tiled per 20 x 20m (no buffer). Cropping and tiling were done with LAStools software. Scan position number was stored as flight line to allow selection of scans if needed. In particular, distant scans which contribute little more than noise could be deleted. LiDAR data were acquired without the “reflectance optimization filter”. In order to keep only returns with reflectance above -20dB (equivalent to setting reflectance optimization filter) all returns with Intensity below 18724 were dropped.", "creationDate": "2023-11-06T15:41:39.765049", "lastUpdatedDate": "2023-11-06T15:30:31", "latestDataUpdateTime": "2025-03-29T01:53:19", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA) by Gregoire Vincent. The UCL project team provided revised metadata for the catalogue record", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Digital twins, Forest structure, 3D tree structure", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-28T15:05:15", "doiPublishedTime": "2025-03-28T16:52:18.303343", "removedDataTime": null, "geographicExtent": { "ob_id": 3953, "bboxName": "UAV Paracou", "eastBoundLongitude": -52.032647, "westBoundLongitude": -52.9211533, "southBoundLatitude": 5.262807, "northBoundLatitude": 5.280352 }, "verticalExtent": null, "result_field": { "ob_id": 40856, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/TLS-Paracou-2021-CNESplot", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 37963466628, "numberOfFiles": 38, "fileFormat": ".laz files" }, "timePeriod": { "ob_id": 12161, "startTime": "2021-10-19T00:00:00", "endTime": "2021-10-29T00:00:00" }, "resultQuality": { "ob_id": 4681, "explanation": "Data quality control conducted by ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-03-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43714, "uuid": "4b29cca847d34921a138a95ceee3196a", "short_code": "cmppr", "title": "ForestScan: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021", "abstract": "ForestScan: Terrestrial Laser Scanning (TLS) of FBRMS-01 Oct 2021" }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" }, { "ob_id": 43715, "uuid": "b5807470b54f405aaabf5b1c7a06e463", "short_code": "proj", "title": "TOSCA Biomass", "abstract": "TOSCA represents the French Earth and environmental sciences community and is tasked with making recommendations to CNES’s Earth sciences team on: The space programme science priorities and direction, funding priorities for space research proposals and, where necessary, any matters concerning space programmes and projects in its science fields\r\n\r\nTOSCA works on science foresight and is consulted by CNES’s Head of Earth Sciences in the event of programmatic changes and on any proposed decision concerning space programs and projects related to this field. Biomass is one TOSCAs scientific programs." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13272 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198714, 198715, 198716, 198717, 198718, 198719, 208906, 208908, 208907 ], "onlineresource_set": [ 94261 ] }, { "ob_id": 40858, "uuid": "5e78ff91e9cd4143bfa3b7358efd2607", "title": "ForestScan: Tree census data (diameter and species name) of FBRMS-01: Paracou, French Guiana 1ha plot IRD-CNES, October 2021", "abstract": "This dataset consists of data collected during the October 2021 census. A few trees were also measured in January 2022 as they could not be accessed in 2021. The data collection includes treeID, position, DBH_cm (girth in cm), observations, POM_cm (Point of measurement) status, census, date, family, genus and species. Botanical identification was done by Julien Engel (IRD). Trees were positioned using TLS scan by Olivier Martin. This tree census was funded by CNES (France).", "creationDate": "2023-11-06T16:14:46.016137", "lastUpdatedDate": "2023-11-06T16:02:29", "latestDataUpdateTime": "2025-03-29T01:53:17", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Tree census data of FBRMS-01: Paracou, French Guiana, plot IRD-CNES", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-28T14:55:01", "doiPublishedTime": "2025-03-28T16:51:47.417139", "removedDataTime": null, "geographicExtent": { "ob_id": 4713, "bboxName": "FBRMS-01: Paracou", "eastBoundLongitude": -52.92604, "westBoundLongitude": -52.92604, "southBoundLatitude": 5.278188, "northBoundLatitude": 5.279276 }, "verticalExtent": null, "result_field": { "ob_id": 40857, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/Inventory-Paracou2021-CNESplot", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 54882, "numberOfFiles": 3, "fileFormat": "Text file" }, "timePeriod": { "ob_id": 12164, "startTime": "2021-10-01T00:00:00", "endTime": "2022-01-31T00:00:00" }, "resultQuality": { "ob_id": 4684, "explanation": "Data quality control conducted by ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-03-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" }, { "ob_id": 43715, "uuid": "b5807470b54f405aaabf5b1c7a06e463", "short_code": "proj", "title": "TOSCA Biomass", "abstract": "TOSCA represents the French Earth and environmental sciences community and is tasked with making recommendations to CNES’s Earth sciences team on: The space programme science priorities and direction, funding priorities for space research proposals and, where necessary, any matters concerning space programmes and projects in its science fields\r\n\r\nTOSCA works on science foresight and is consulted by CNES’s Head of Earth Sciences in the event of programmatic changes and on any proposed decision concerning space programs and projects related to this field. Biomass is one TOSCAs scientific programs." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13271 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198720, 198721, 198722, 198723, 198724, 198725, 208915, 208916, 209165, 209166 ], "onlineresource_set": [ 94262 ] }, { "ob_id": 40859, "uuid": "15a170dad3064fefa8936bd50877a93e", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Microwave Scanning Radiometer (AMSR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Microwave Scanning Radiometers (AMSR), presented on the native geometry of observation (Level 2), and spanning 2002 to 2017. \r\n\r\nThe SST CCI AMSR product contains two different SST estimates. The first is the subskin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. Additionally, the AMSR files contain a satellite estimate of the surface wind speed. \r\n \r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-05-10T01:48:34", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, AMSR, L2P", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "completed", "dataPublishedTime": "2024-05-07T15:26:36", "doiPublishedTime": "2024-05-07T16:01:38", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41573, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AMSR/L2P/v3.0.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 419983429851, "numberOfFiles": 154471, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11565, "startTime": "2002-06-01T00:00:00", "endTime": "2017-10-26T23:59:59" }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41576, "uuid": "3d4f0c5ffeb344b3802b16245cfd129b", "short_code": "cmppr", "title": "Composite process for ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Microwave Scanning Radiometer (AMSR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Microwave Scanning Radiometer (AMSR) Level 2 Pre-processed (L2P) product, version 3.0 is derived from information from the AMSR-2 and AMSR-E satellite instruments on board the GCOM-W and EOS Aqua satellites respectively.\r\n\r\nFor more information on their derivation see the CCI project documentation." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52529, 52531, 52533, 52535, 52536, 52542, 57989, 66265, 66268, 66269, 66270, 66274, 66275, 66276, 66277, 74116, 74117, 74118, 74119 ], "vocabularyKeywords": [], "identifier_set": [ 12865 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." } ], "responsiblepartyinfo_set": [ 198732, 198726, 198734, 198727, 198728, 198729, 198731, 198733, 202192, 202193, 202194 ], "onlineresource_set": [ 85039, 86158, 86159, 86160, 86556 ] }, { "ob_id": 40860, "uuid": "ec659b31a8ca40918e58ec6d03af07a6", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on the native geometry of observation, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-05-18T01:49:50", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "SST, ESA Climate Change Initiative, CCI", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "completed", "dataPublishedTime": "2024-05-15T11:04:19", "doiPublishedTime": "2024-05-16T13:20:56", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41654, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AVHRR/L2P/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 30578363976842, "numberOfFiles": 546261, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11564, "startTime": "1979-07-13T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4527, "explanation": "For information on the data quality see the related documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41655, "uuid": "575ac123104d4039be109690608bd526", "short_code": "cmppr", "title": "CCI SST retrieval process for the CDR v3 datasets from the AVHRR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the AVHRR series of satellite instruments. This process describes the CDR v3 versions of these datasets." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52527, 52531, 52533, 52535, 52536, 52545, 57989, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [], "identifier_set": [ 12911 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198739, 198740, 198741, 198742, 198743, 198744, 198745, 198746, 202191 ], "onlineresource_set": [ 85045, 86280, 86281, 86282, 86557 ] }, { "ob_id": 40861, "uuid": "c1d393f990fb4b6688b048222833d92f", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on a 0.05° latitude-longitude grid, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-07T01:52:35", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, SST, AVHRR, L3U", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "ongoing", "dataPublishedTime": "2024-05-16T13:23:57", "doiPublishedTime": "2024-05-16T13:24:38", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41653, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AVHRR/L3U/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5712863793867, "numberOfFiles": 546195, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11566, "startTime": "1979-07-13T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4529, "explanation": "For information on the data quality see the related documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2024-02-22" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41655, "uuid": "575ac123104d4039be109690608bd526", "short_code": "cmppr", "title": "CCI SST retrieval process for the CDR v3 datasets from the AVHRR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the AVHRR series of satellite instruments. This process describes the CDR v3 versions of these datasets." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52530, 52532, 52535, 52536, 52545, 57989, 66255, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [], "identifier_set": [ 12916 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198747, 198748, 198749, 198750, 198751, 198752, 198753, 198754, 202195 ], "onlineresource_set": [ 85053, 86277, 86278, 86279, 86559 ] }, { "ob_id": 40862, "uuid": "be418645dfa542df86165a7caad24284", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), daily collations on a 0.05° latitude-longitude grid, spanning 1980 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-10T01:58:00", "updateFrequency": "irregular", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving in the context of the ESA CCI Open Data Portal project and the National Centre for Earth Observation (NCEO).", "removedDataReason": "", "keywords": "ESA, CCI, SST, AVHRR, L3C", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "ongoing", "dataPublishedTime": "2024-05-16T08:07:48", "doiPublishedTime": "2024-05-16T13:21:58", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41652, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AVHRR/L3C/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4850214203266, "numberOfFiles": 72355, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11567, "startTime": "1979-07-13T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41655, "uuid": "575ac123104d4039be109690608bd526", "short_code": "cmppr", "title": "CCI SST retrieval process for the CDR v3 datasets from the AVHRR series of instruments", "abstract": "The ESA Climate Change Initiative Sea Surface Temperature (SST) product has retrieved sea surface temperature from the AVHRR series of satellite instruments. This process describes the CDR v3 versions of these datasets." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." }, { "ob_id": 43216, "uuid": "8bffaba46c4a4b8c82e4be2c91c637b9", "short_code": "proj", "title": "Earth Observation Climate Information Service (EOCIS)", "abstract": "The UK Earth Observation Climate Information Service exploits the observations available from environmental sensors orbiting in space to create climate data records and climate information. EOCIS was announced by the government in November 2022, and formally launched in March 2023. It is funded currently until March 2025. \r\n\r\nEOCIS is a collaboration led by the National Centre for Earth Observation, and involving over a dozen research organisations. EOCIS addresses 12 categories of global and regional essential climate variables, which are the following:\r\n- Sea surface temperature\r\n- Ocean reflectance\r\n- Fire occurrence and emissions\r\n- Aerosol and particulate\r\n- Cloud-aerosol-radiation\r\n- Methane\r\n- Land surface temperature\r\n- Water vapour, ozone\r\n- Arctic: ice sheet mass and sea ice\r\n- Eurasia: surface methane\r\n- Africa: soil water balance\r\n- Antarctic: ice sheet mass and ice velocity\r\n\r\nEOCIS is also creating new climate data at high resolution for the UK specifically. This includes both rapid-response information for climate-linked events (fire early warning and urban flood mapping) and longer term climate data linked to human and ecosystem health and landscape greenhouse gas emissions." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52530, 52532, 52535, 52536, 52545, 57989, 66255, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [], "identifier_set": [ 12912 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198755, 198756, 198757, 198758, 198759, 198760, 198761, 198762, 202196, 202198, 202197 ], "onlineresource_set": [ 85059, 86274, 86275, 86276, 86558 ] }, { "ob_id": 40863, "uuid": "f4151599eb7b491c9f4ce75489eb8b1e", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 2 Pre-processed (L2P) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), presented on the native geometry of observation, and spanning 2016 to 2021. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-08T02:29:03", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, SLSTR, L2P", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-05-16T08:02:02", "doiPublishedTime": "2024-05-16T13:22:16", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41651, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L2P/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5489899353001, "numberOfFiles": 1208393, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11568, "startTime": "2016-06-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41650, "uuid": "f65ab8a5be5f4685b8a6d1a9017e7a49", "short_code": "cmppr", "title": "Composite process for ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, v3.0", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, version 3.0 are derived from the SLSTR instrument on board the Sentinel 3A and 3B satellites.\r\n\r\nFor more information on their derivation see the Algorithm Theoretical Baseline Document (ATBD) in the project documentation." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 52527, 52531, 52533, 52535, 52536, 52545, 57989, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [], "identifier_set": [ 12913 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198763, 198764, 198765, 198766, 198767, 198768, 198769, 198770, 202199 ], "onlineresource_set": [ 85065, 86271, 86272, 86273, 86567 ] }, { "ob_id": 40864, "uuid": "61b7a51d72b54692890d45818307d72f", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 3 Uncollated (L3U) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), presented on a 0.05° latitude-longitude grid, and spanning 2016 to 2021. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-08T02:29:04", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, SLSTR, L3U", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.05 degree", "status": "completed", "dataPublishedTime": "2024-05-15T06:55:27", "doiPublishedTime": "2024-05-16T13:22:32", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41647, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L3U/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 986265904667, "numberOfFiles": 1206330, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11659, "startTime": "2016-01-01T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41650, "uuid": "f65ab8a5be5f4685b8a6d1a9017e7a49", "short_code": "cmppr", "title": "Composite process for ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, v3.0", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, version 3.0 are derived from the SLSTR instrument on board the Sentinel 3A and 3B satellites.\r\n\r\nFor more information on their derivation see the Algorithm Theoretical Baseline Document (ATBD) in the project documentation." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52530, 52532, 52535, 52536, 52545, 57989, 66255, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [], "identifier_set": [ 12914 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198771, 198772, 198773, 198774, 198775, 198776, 198777, 198778, 202200 ], "onlineresource_set": [ 85071, 86267, 86268, 86269, 86566 ] }, { "ob_id": 40865, "uuid": "a104ed92bddd4c56b11127d4cc49b8d4", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 3 Collated (L3C) product, version 3.0", "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), daily collations on a 0.05° latitude-longitude grid, spanning 2016 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n \r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-01-08T02:29:02", "updateFrequency": "irregular", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, SLSTR, L3C", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-05-15T06:58:33", "doiPublishedTime": "2024-05-16T13:22:50", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41646, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L3C/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 292308422299, "numberOfFiles": 10179, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11569, "startTime": "2016-06-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41650, "uuid": "f65ab8a5be5f4685b8a6d1a9017e7a49", "short_code": "cmppr", "title": "Composite process for ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, v3.0", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (SST_cci): SLSTR datasets, version 3.0 are derived from the SLSTR instrument on board the Sentinel 3A and 3B satellites.\r\n\r\nFor more information on their derivation see the Algorithm Theoretical Baseline Document (ATBD) in the project documentation." }, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." }, { "ob_id": 43216, "uuid": "8bffaba46c4a4b8c82e4be2c91c637b9", "short_code": "proj", "title": "Earth Observation Climate Information Service (EOCIS)", "abstract": "The UK Earth Observation Climate Information Service exploits the observations available from environmental sensors orbiting in space to create climate data records and climate information. EOCIS was announced by the government in November 2022, and formally launched in March 2023. It is funded currently until March 2025. \r\n\r\nEOCIS is a collaboration led by the National Centre for Earth Observation, and involving over a dozen research organisations. EOCIS addresses 12 categories of global and regional essential climate variables, which are the following:\r\n- Sea surface temperature\r\n- Ocean reflectance\r\n- Fire occurrence and emissions\r\n- Aerosol and particulate\r\n- Cloud-aerosol-radiation\r\n- Methane\r\n- Land surface temperature\r\n- Water vapour, ozone\r\n- Arctic: ice sheet mass and sea ice\r\n- Eurasia: surface methane\r\n- Africa: soil water balance\r\n- Antarctic: ice sheet mass and ice velocity\r\n\r\nEOCIS is also creating new climate data at high resolution for the UK specifically. This includes both rapid-response information for climate-linked events (fire early warning and urban flood mapping) and longer term climate data linked to human and ecosystem health and landscape greenhouse gas emissions." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52527, 52530, 52532, 52535, 52536, 52545, 57989, 66255, 66261, 66262, 66264, 66265, 66266, 66267, 66268, 66269, 66270, 66271, 66272, 66273, 66274, 66275, 66276, 66277, 66278, 66279, 74117 ], "vocabularyKeywords": [ { "ob_id": 10337, "vocabService": "clipc_skos_vocab", "uri": "http://vocab-test.ceda.ac.uk/collection/cci/dataType/dtype_sstInt", "resolvedTerm": "sea surface temperature" } ], "identifier_set": [ 12915 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198779, 198780, 198781, 198782, 198783, 198784, 198785, 198786, 202201, 202202, 202203 ], "onlineresource_set": [ 85077, 86264, 86265, 86266, 86565 ] }, { "ob_id": 40866, "uuid": "4a9654136a7148e39b7feb56f8bb02d2", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0", "abstract": "This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present. This is a Level 4 product, with gaps between available daily observations filled by statistical means.\r\n\r\nThe SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-12-17T14:03:02", "updateFrequency": "irregular", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, Analysis, Level 4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2024-04-08T14:27:36", "doiPublishedTime": "2024-04-09T08:40:02", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41571, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/Analysis/L4/v3.0.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 261436589158, "numberOfFiles": 16246, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11614, "startTime": "1980-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41572, "uuid": "dcabd9408c9f42888c11746895d7ce09", "short_code": "comp", "title": "Derivation of the CCI SST Analysis v3 product", "abstract": "For information on the derivation of the SST CCI Analysis v3 product, see the SST CCI project documentation" }, "procedureCompositeProcess": null, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." }, { "ob_id": 43216, "uuid": "8bffaba46c4a4b8c82e4be2c91c637b9", "short_code": "proj", "title": "Earth Observation Climate Information Service (EOCIS)", "abstract": "The UK Earth Observation Climate Information Service exploits the observations available from environmental sensors orbiting in space to create climate data records and climate information. EOCIS was announced by the government in November 2022, and formally launched in March 2023. It is funded currently until March 2025. \r\n\r\nEOCIS is a collaboration led by the National Centre for Earth Observation, and involving over a dozen research organisations. EOCIS addresses 12 categories of global and regional essential climate variables, which are the following:\r\n- Sea surface temperature\r\n- Ocean reflectance\r\n- Fire occurrence and emissions\r\n- Aerosol and particulate\r\n- Cloud-aerosol-radiation\r\n- Methane\r\n- Land surface temperature\r\n- Water vapour, ozone\r\n- Arctic: ice sheet mass and sea ice\r\n- Eurasia: surface methane\r\n- Africa: soil water balance\r\n- Antarctic: ice sheet mass and ice velocity\r\n\r\nEOCIS is also creating new climate data at high resolution for the UK specifically. This includes both rapid-response information for climate-linked events (fire early warning and urban flood mapping) and longer term climate data linked to human and ecosystem health and landscape greenhouse gas emissions." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 52530, 52532, 66255, 66257, 66258, 66259, 66260, 75580 ], "vocabularyKeywords": [], "identifier_set": [ 12857 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198792, 198793, 198794, 198787, 198788, 198789, 198790, 198791, 202204, 202206, 202205, 202207 ], "onlineresource_set": [ 85083, 86155, 86156, 86157, 86564, 92695 ] }, { "ob_id": 40867, "uuid": "62800d3d2227449085b430b503d36b01", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology product, version 3.0", "abstract": "This dataset provides daily climatological mean sea surface temperature (SST) on a global 0.05° latitude-longitude grid, derived from the SST CCI analysis data for the period 1991 to 2020 (30 years). \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.2 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n* Improved retrieval with respect to desert-dust aerosols \r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-04-10T01:50:38", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI SST project team and supplied to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "ESA, CCI, SST, Climatology, Level 4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2024-04-08T14:00:32", "doiPublishedTime": "2024-04-09T08:40:06", "removedDataTime": null, "geographicExtent": { "ob_id": 1, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 41569, "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/Climatology/L4/v3.0.1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15119298879, "numberOfFiles": 366, "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification." }, "timePeriod": { "ob_id": 11570, "startTime": "1991-01-01T00:00:00", "endTime": "2020-12-31T23:59:59" }, "resultQuality": { "ob_id": 4552, "explanation": "For information on data quality see the linked Product Validation and Intercomparison report (PVIR) and the Climate Assessment Report (CAR)", "passesTest": true, "resultTitle": "CCI SST v3", "date": "2024-04-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 41570, "uuid": "4d536da9cce344a58056e974001e86cb", "short_code": "comp", "title": "Derivation of the CCI SST Climatology v3 product", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative: Climatology, v3 product was derived from the SST CCI analysis data for the period 1991 to 2020 (30 years).\r\n\r\nFor more information see the SST CCI project documentation" }, "procedureCompositeProcess": null, "imageDetails": [ 137 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2523, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 4, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sst_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11008, "uuid": "05fb7c9964b4172991a72082c46a3376", "short_code": "proj", "title": "Sea Surface Temperature Climate Change Initiative Project", "abstract": "The Sea Surface Temperature Climate Change Initiative (SST_cci) project is part of the European Space Agency's Climate Change Initiative programme, It aims to accurately mapping the surface temperature of the global oceans using observations from many satellites, and to independently quantify SST to a quality suitable for climate research.\r\n\r\nThe team brings together European expertise in creating climate quality records of ocean temperatures from satellite data, with expertise in climate applications and computer engineering. Through the ESA funded Climate Change Initiative, the team have created a climate record of global sea surface temperature (SST) for the period 1981 to 2016. Based on satellite data, this record is independent of thermometer based measurements from ships and buoys. The new climate SST record complements and challenges existing knowledge of how ocean temperatures have evolved. \r\n\r\nThe project started in August 2010. It is part of a wider initiative by the European Space Agency (ESA) addressing several essential climate variables in addition to SST." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50415, 50417, 60312, 60313, 66257, 66260, 75578, 75579, 75580 ], "vocabularyKeywords": [], "identifier_set": [ 12858 ], "observationcollection_set": [ { "ob_id": 41661, "uuid": "debfbf49823f4eb99ab0a578f8b25136", "short_code": "coll", "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climate Data Record version 3.0", "abstract": "The European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature (ESA SST_cci) Climate Data Record (CDR) accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and UK Marine and Climate Advisory Service (UKMCAS).\r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are:\r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016)\r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors)\r\n\r\n* Improved retrieval with respect to desert-dust aerosols\r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards\r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s\r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)\r\n\r\n* Inclusion of L2P passive microwave AMSR data\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/\r\n\r\nWhen citing this dataset please also cite the associated data paper:\r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w" }, { "ob_id": 11005, "uuid": "1dc189bbf94209b48ed446c0e9a078af", "short_code": "coll", "title": "Collection of Sea Surface Temperature (SST) Data of the Global Oceans as part of the ESA Climate Change Initiative (CCI)", "abstract": "The ESA Sea Surface Temperature Climate Change Initiative (ESA SST_cci) datasets accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research.\r\n\r\nThe latest version (v2.1) of the data are described in the data paper: Merchant, C.J., Embury, O., Bulgin, C.E., Block T., Corlett, G.K., Fiedler, E., Good, S.A., Mittaz, J., Rayner, N.A., Berry, D., Eastwood, S., Taylor, M., Tsushima, Y., Waterfall, A., Wilson, R., Donlon, C. Satellite-based time-series of sea-surface temperature since 1981 for climate applications, Scientific Data 6:223 (2019). http://doi.org/10.1038/s41597-019-0236-x\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ . To comply with the attribution aspect, please cite the above reference and the dataset citation given on the relevant dataset page." }, { "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": [ 198795, 198796, 198797, 198798, 198799, 198800, 198801, 198802, 202208, 202209 ], "onlineresource_set": [ 86152, 86153, 86154, 86151, 86563 ] }, { "ob_id": 40868, "uuid": "7a4649cabd3e4afb8cd31cfd7d95ac8e", "title": "ForestScan project: Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon, June 2022", "abstract": "This dataset contains point cloud data (a set of data points in a 3D coordinate system) which were collected using a RIEGL miniVUX1-DL LiDAR scanner mounted on a DELAIR DT26X Unpiloted Aerial Vehicle (UAV). The data was collected in June 2022 as part of the ForestScan project. The person responsible for the data collection was Dr. Iain McNicol from the University of Edinburgh, who collected and processed the data.", "creationDate": "2023-11-06T20:00:46.659796", "lastUpdatedDate": "2023-11-06T19:55:00", "latestDataUpdateTime": "2023-08-17T17:02:40", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA). Revised metadata was also provided by the project team", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "deprecated", "dataPublishedTime": "2025-03-28T15:00:05", "doiPublishedTime": "2025-03-28T16:52:49", "removedDataTime": null, "geographicExtent": { "ob_id": 4712, "bboxName": "FBRMS-02: Station d’Etudes des Gorilles", "eastBoundLongitude": -0.1896, "westBoundLongitude": -0.1896, "southBoundLatitude": 11.5918, "northBoundLatitude": 11.5918 }, "verticalExtent": null, "result_field": { "ob_id": 43648, "dataPath": "/neodc/forestscan/data/gabon/lope/UAV_PointCloudData_lope_2022", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15795008315, "numberOfFiles": 151, "fileFormat": "Point cloud data .las files" }, "timePeriod": { "ob_id": 12162, "startTime": "2022-06-01T00:00:00", "endTime": "2022-06-12T18:33:05" }, "resultQuality": { "ob_id": 4682, "explanation": "Data quality control conducted by ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-03-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 43651, "uuid": "0e5edec3208d43b3a198728778105b40", "short_code": "acq", "title": "Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon, June 2022", "abstract": "ForestScan project: Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS) data of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon, June 2022" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13276 ], "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": [ 198803, 198804, 198805, 198806, 198807, 198808, 208912, 208914, 208913 ], "onlineresource_set": [ 94263 ] }, { "ob_id": 40870, "uuid": "1d554ff41c104491ac3661c6f6f52aab", "title": "Aerial LiDAR data from French Guiana, Paracou, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Paracou in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrume was a RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80% with a scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Paracou site can be found in the documentation section.", "creationDate": "2023-11-06T20:51:17.829155", "lastUpdatedDate": "2023-11-06T20:48:09", "latestDataUpdateTime": "2024-03-09T03:19:41", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ALS, Aerial Lidar , Biomass, Paracou, Frech Guiana", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-12-20T16:00:30", "doiPublishedTime": "2023-12-20T18:20:40.279957", "removedDataTime": null, "geographicExtent": { "ob_id": 3953, "bboxName": "UAV Paracou", "eastBoundLongitude": -52.032647, "westBoundLongitude": -52.9211533, "southBoundLatitude": 5.262807, "northBoundLatitude": 5.280352 }, "verticalExtent": null, "result_field": { "ob_id": 40871, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/ALS-Paracou-2019", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7507935643, "numberOfFiles": 184, "fileFormat": ".laz files" }, "timePeriod": { "ob_id": 11451, "startTime": "2019-11-15T00:00:00", "endTime": "2019-11-16T00:00:00" }, "resultQuality": { "ob_id": 4471, "explanation": "Data were validated by Toby Jackson project team and provided to CEDA for archival.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-12-19" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41240, "uuid": "280d67b54d254727abf2de7092d8607a", "short_code": "acq", "title": "Paracou ALS Nov 2019", "abstract": "A LiDAR instrument: RIEGL LMS-Q780 used a minimum pulse density: 15 points/sqm. Lateral overlap between two flight lines: 80%. Scan angle: +/- 30 degrees." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38113, "uuid": "ff6cc7ada8824ce3bbec1e777a5e0850", "short_code": "proj", "title": "A 3D perspective of the effects of topography and wind on forest height and dynamics", "abstract": "This project aims to track how forest canopy height (and therefore biomass) is changing over time and whether this is correlated to local topography or wind patterns. We collected LiDAR and RGB data in areas with similar pre-existing data sets in order to detect changes over time. NE/S010750/1" }, { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12796 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" } ], "responsiblepartyinfo_set": [ 198809, 198810, 198811, 198812, 198813, 198814, 200714, 200717, 200718, 200715, 200716 ], "onlineresource_set": [ 85659, 85660, 94264 ] }, { "ob_id": 40872, "uuid": "7bdc5bfc06264802be34f918597150e8", "title": "Aerial LiDAR data from French Guiana, Nouragues, November 2019", "abstract": "This dataset contains Aerial LiDAR (also known as airborne laser scanning, ALS) data in .las format collected over tropical forests in Nouragues in French Guiana in 2019. The data were collected by Altoa using a BN2 aircraft flying at approximately 900 m altitude at a speed of approximately 180 km/hr. Trajectory files in txt format giving detailed flight data are included with the archived dataset. The LiDAR instrument was RIEGL LMS-Q780 and used a minimum pulse density of 15 points/sqm. The lateral overlap between two flight lines was 80%. with a Scan angle of +/- 30 degrees. The data coordinate reference system used with the data files is epsg 2972 more details of this and of the Nouragues site can be found in the documentation section.", "creationDate": "2023-11-06T20:51:17.829155", "lastUpdatedDate": "2023-11-06T20:48:09", "latestDataUpdateTime": "2024-09-11T13:10:28", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ALS, Aerial Lidar , Biomass, Nouragues, Frech Guiana", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2023-12-20T15:52:33", "doiPublishedTime": "2023-12-20T18:21:54.474489", "removedDataTime": null, "geographicExtent": { "ob_id": 2294, "bboxName": "NOU-11 French Guina plot site", "eastBoundLongitude": -52.68, "westBoundLongitude": -52.68, "southBoundLatitude": 4.08, "northBoundLatitude": 4.08 }, "verticalExtent": null, "result_field": { "ob_id": 40873, "dataPath": "/neodc/forestscan/data/french_guiana/nourages/ALS-Nouragues-2019-PerPlot", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 17314433281, "numberOfFiles": 497, "fileFormat": ".laz files and text file with trajectory details" }, "timePeriod": { "ob_id": 11454, "startTime": "2019-11-15T00:00:00", "endTime": "2019-11-16T00:00:00" }, "resultQuality": { "ob_id": 4472, "explanation": "Data were validated by Toby Jackson project team and provided to CEDA for archival.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-12-19" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41240, "uuid": "280d67b54d254727abf2de7092d8607a", "short_code": "acq", "title": "Paracou ALS Nov 2019", "abstract": "A LiDAR instrument: RIEGL LMS-Q780 used a minimum pulse density: 15 points/sqm. Lateral overlap between two flight lines: 80%. Scan angle: +/- 30 degrees." }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 38113, "uuid": "ff6cc7ada8824ce3bbec1e777a5e0850", "short_code": "proj", "title": "A 3D perspective of the effects of topography and wind on forest height and dynamics", "abstract": "This project aims to track how forest canopy height (and therefore biomass) is changing over time and whether this is correlated to local topography or wind patterns. We collected LiDAR and RGB data in areas with similar pre-existing data sets in order to detect changes over time. NE/S010750/1" }, { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 12797 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" } ], "responsiblepartyinfo_set": [ 198815, 198816, 198817, 198818, 198819, 198820, 200719, 200722, 200723, 200720, 200721 ], "onlineresource_set": [ 85663, 85664, 94265 ] }, { "ob_id": 40874, "uuid": "656ac8ee1d42443f9addcbce28c1b137", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-01: Paracou, French Guiana 1ha plot FG5c1, September to October 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in French Guiana from September to October 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. Data collection assistance was provided by UCL PhD student Wanxin Yang and a local team of field assistants, data processing assistance was provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing terrestial (TLS), unpiloted airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the three FBRMS plots is found within plot directories: FG5c1, FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-10-10_FG5c1.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG5c1/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.", "creationDate": "2023-11-06T21:21:41.246930", "lastUpdatedDate": "2023-11-06T21:20:10", "latestDataUpdateTime": "2025-02-24T13:57:54", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, ESA, AGB estimates, TLS, UAV-LS, ALS, Tropical forests, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "planned", "dataPublishedTime": "2025-03-27T12:14:51", "doiPublishedTime": "2025-03-28T16:55:17.135293", "removedDataTime": null, "geographicExtent": { "ob_id": 4698, "bboxName": "(TLS) FG5c1", "eastBoundLongitude": -52.929093, "westBoundLongitude": -52.929739, "southBoundLatitude": 5.271558, "northBoundLatitude": 5.272639 }, "verticalExtent": null, "result_field": { "ob_id": 43556, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG5c1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1467542700932, "numberOfFiles": 1282074, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the archive /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG5c1/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12105, "startTime": "2022-10-10T00:00:00", "endTime": "2022-10-17T00:00:00" }, "resultQuality": { "ob_id": 4669, "explanation": "Data quality control conducted by UCL ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-02-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43559, "uuid": "175baa262021485fbce83af21087dfbb", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan plots in French Guiana", "abstract": "Terrestrial laser scan composite process for ForestScan plots in French Guiana" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13287 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198821, 198822, 198823, 198824, 198825, 198826, 207988, 207991, 207989, 207990, 208322, 208323, 208324, 208325, 208326, 208327, 208328, 208329, 208330, 208331, 208332, 208333, 208336, 208337, 208338, 208339, 208341, 208342, 208343, 208344, 208345, 208340, 208353, 208348, 208349, 208355, 208350, 208356, 208357, 208351, 208346, 208335, 208334, 208352, 208354, 208358, 208359 ], "onlineresource_set": [ 94266 ] }, { "ob_id": 40875, "uuid": "931973db09af41568853702efe135f29", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-01: Paracou, French Guiana 1ha plot FG6c2, September to October 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in French Guiana from September to October 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the three FBRMS plots is found within plot directories: FG5c1, FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-10-18_FG6c2.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG6c2/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.", "creationDate": "2023-11-06T21:29:13.138247", "lastUpdatedDate": "2023-11-06T21:23:35", "latestDataUpdateTime": "2025-02-24T13:58:09", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-27T12:16:02", "doiPublishedTime": "2025-03-28T16:55:13.990894", "removedDataTime": null, "geographicExtent": { "ob_id": 4699, "bboxName": "TLS FG6c2", "eastBoundLongitude": -52.925218, "westBoundLongitude": -52.9258, "southBoundLatitude": 5.272522, "northBoundLatitude": 5.273609 }, "verticalExtent": null, "result_field": { "ob_id": 43560, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG6c2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1522120454888, "numberOfFiles": 768701, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the in the following directory /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG6c2/.ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12106, "startTime": "2022-10-18T00:00:00", "endTime": "2022-10-25T00:00:00" }, "resultQuality": { "ob_id": 4686, "explanation": "Data quality control conducted by UCL ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-03-16" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43559, "uuid": "175baa262021485fbce83af21087dfbb", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan plots in French Guiana", "abstract": "Terrestrial laser scan composite process for ForestScan plots in French Guiana" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13286 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198831, 198832, 207992, 207995, 198827, 198828, 198829, 198830, 207993, 207994, 208378, 208379, 208380, 208381, 208382, 208383, 208384, 208385, 208386, 208387, 208388, 208389, 208392, 208393, 208394, 208395, 208397, 208398, 208399, 208400, 208401, 208396, 208409, 208404, 208405, 208411, 208406, 208412, 208413, 208407, 208402, 208391, 208390, 208408, 208410, 208414, 208415 ], "onlineresource_set": [ 94267 ] }, { "ob_id": 40876, "uuid": "40f0f38023ac40f6b40bbf96e4dc5258", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-01: Paracou, French Guiana 1ha plot FG8c4, September to October 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in French Guiana from September to October 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the three FBRMS plots is found within plot directories: FG5c1, FG6c2 and FG8c4. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-09-26_FG8c4.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG8c4/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.", "creationDate": "2023-11-06T21:33:13.731305", "lastUpdatedDate": "2023-11-06T21:31:59", "latestDataUpdateTime": "2025-02-24T13:58:26", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-27T13:51:09", "doiPublishedTime": "2025-03-28T16:55:10.982432", "removedDataTime": null, "geographicExtent": { "ob_id": 4701, "bboxName": "TLS FG8c4", "eastBoundLongitude": -52.929609, "westBoundLongitude": -52.93027, "southBoundLatitude": 5.262988, "northBoundLatitude": 5.264127 }, "verticalExtent": null, "result_field": { "ob_id": 43561, "dataPath": "/neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG8c4/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1511278928546, "numberOfFiles": 1025581, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the following archived document /neodc/forestscan/data/french_guiana/paracou/TLS_Plot_FG8c4/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12107, "startTime": "2022-09-26T00:00:00", "endTime": "2022-10-06T00:00:00" }, "resultQuality": { "ob_id": 4670, "explanation": "Data quality control conducted by UCL ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-02-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43559, "uuid": "175baa262021485fbce83af21087dfbb", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan plots in French Guiana", "abstract": "Terrestrial laser scan composite process for ForestScan plots in French Guiana" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13285 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198833, 198834, 198835, 198836, 198837, 198838, 207996, 207999, 207997, 207998, 208417, 208418, 208419, 208420, 208421, 208422, 208423, 208424, 208425, 208426, 208427, 208428, 208431, 208432, 208433, 208434, 208436, 208437, 208438, 208439, 208440, 208435, 208448, 208443, 208444, 208450, 208445, 208451, 208452, 208446, 208441, 208430, 208429, 208447, 208449, 208453, 208454 ], "onlineresource_set": [ 94268 ] }, { "ob_id": 40878, "uuid": "45ae3437f82f4e4fb75f9a5c26a194ba", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1ha plot OKO-01, June to July 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in Gabon from June to July 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. Data collection assistance was provided by UCL postdoc Dr Phil Wilkes and a local team of field assistants, data processing assistance was provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the four FBRMS plots is found within plot directories: LPG-01, OKO-01, OKO-02 and OKO-03. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-06-04_OKO-01.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-01/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.", "creationDate": "2023-11-06T21:42:42.668115", "lastUpdatedDate": "2023-11-06T21:41:56", "latestDataUpdateTime": "2025-03-29T01:53:09", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-27T15:17:26", "doiPublishedTime": "2025-03-28T16:55:04.888702", "removedDataTime": null, "geographicExtent": { "ob_id": 2372, "bboxName": "TLS - OKO -01 Gabon Ogooué-Ivindo Lopé", "eastBoundLongitude": 11.583, "westBoundLongitude": 11.583, "southBoundLatitude": -0.196, "northBoundLatitude": -0.196 }, "verticalExtent": null, "result_field": { "ob_id": 43566, "dataPath": "/neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-01", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1410567879717, "numberOfFiles": 816262, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-01/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12109, "startTime": "2022-06-04T00:00:00", "endTime": "2022-06-09T00:00:00" }, "resultQuality": { "ob_id": 4672, "explanation": "Data quality control conducted by UCL ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-02-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43565, "uuid": "c384fdd708e44dc29371d30ba2ace0a7", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan Plots in Gabon", "abstract": "Terrestrial laser scan composite process for ForestScan Plots in Gabon" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13283 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198845, 198846, 198847, 198848, 198849, 198850, 208007, 208010, 208008, 208009, 208506, 208507, 208508, 208509, 208510, 208511, 208512, 208513, 208514, 208515, 208516, 208517, 208520, 208521, 208522, 208523, 208525, 208526, 208527, 208528, 208529, 208524, 208537, 208532, 208533, 208539, 208534, 208540, 208541, 208535, 208530, 208519, 208518, 208536, 208538, 208542, 208543 ], "onlineresource_set": [ 94269 ] }, { "ob_id": 40879, "uuid": "ff4b43475c9641cca1dad2c8be8dadaf", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1ha plot OKO-02, June to July 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in Gabon from June to July 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. Data collection assistance was provided by Heddy O. Milamizokou Napo, Luna Soenens and Virginie Daelemans, data processing assistance was provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the four FBRMS plots is found within plot directories: LPG-01, OKO-01, OKO-02 and OKO-03. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-06-10_OKO-02.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-02/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets", "creationDate": "2023-11-06T21:46:03.761010", "lastUpdatedDate": "2023-11-06T21:45:11", "latestDataUpdateTime": "2025-03-29T01:54:35", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-27T15:24:15", "doiPublishedTime": "2025-03-28T16:54:19.283572", "removedDataTime": null, "geographicExtent": { "ob_id": 4702, "bboxName": "TLS OKO-02 Gabon", "eastBoundLongitude": 11.582236, "westBoundLongitude": 11.582133, "southBoundLatitude": -0.190773, "northBoundLatitude": -0.189509 }, "verticalExtent": null, "result_field": { "ob_id": 43567, "dataPath": "/neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-02/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1527859727292, "numberOfFiles": 831131, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-02/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12110, "startTime": "2022-06-10T00:00:00", "endTime": "2022-06-23T00:00:00" }, "resultQuality": { "ob_id": 4673, "explanation": "Data quality control conducted by UCL ForestScan project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2025-02-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43565, "uuid": "c384fdd708e44dc29371d30ba2ace0a7", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan Plots in Gabon", "abstract": "Terrestrial laser scan composite process for ForestScan Plots in Gabon" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13282 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198851, 198852, 198853, 198854, 198855, 198856, 208011, 208014, 208012, 208013, 208544, 208545, 208546, 208547, 208548, 208549, 208550, 208551, 208552, 208553, 208554, 208555, 208558, 208559, 208560, 208561, 208563, 208564, 208565, 208566, 208567, 208562, 208575, 208570, 208571, 208577, 208572, 208578, 208579, 208573, 208568, 208557, 208556, 208574, 208576, 208580, 208581 ], "onlineresource_set": [ 94270 ] }, { "ob_id": 40880, "uuid": "8ed3ddec76b8470285bdb2ea643f54bc", "title": "ForestScan Project : Terrestrial Laser Scanning (TLS) of FBRMS-02: Station d’Etudes des Gorilles et Chimpanzés, Lopé National Park, Gabon 1ha plot OKO-03, June to July 2022", "abstract": "Terrestrial laser scanning (TLS) was conducted at three ForestScan 1ha (100m x 100m) Forest Biomass Reference Measurement Site (FBRMS) plots in Gabon from June to July 2022 by Cecilia Chavana-Bryant using a Riegl VZ-400i scanner. Data collection assistance was provided by Heddy O. Milamizokou Napo, Luna Soenens and Virginie Daelemans, data processing assistance was provided by Mr Peter Vines. This data collection was part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates.\r\n\r\nScans were acquired using chain sampling at 121 locations along a 10m Cartesian grid to ensure sufficient data overlap to produce high-quality point clouds for all ForestScan 1ha FBRMS plots. Due to the scanner's 100° field of view, capturing a complete sample of the scene at each scan location required two scans -an upright scan and a tilt scan. Upright scans are odd-numbered while tilt scans are even-numbered. The first scan at each plot is collected at the southwest corner, i.e. scan position 0,0 (unless something impedes it, e.g. stream, large tree fall, etc. or if the plot is oriented differently). To facilitate scan registration, five retro-reflective targets were located between scan positions with all tilt scans along the first sampling line were oriented towards the same sampling position along the next sampling line and tilt scans at the ends of sampling lines (i.e. tilt scans along plot edges) were oriented towards the inside of the plot. This aids scan registration as it allows tilt scans to capture the previous scan location within its field of view. A total of 242 scans were collected at each plot.\r\n\r\nThe Riegl operating and processing software RiSCAN PRO version 2.14.1 was used to generate a plot-level point cloud, scans were coarse registered using the shared retro-reflective targets located between consecutive scan positions. Coarse registration was then fine-tuned using Multi Station Adjustment 2 (MSA2).\r\n\r\nData for each of the four FBRMS plots is found within plot directories: LPG-01, OKO-01, OKO-02 and OKO-03. Plot directories contain a main project directory (named using the starting date of data collection, e.g. 2022-07-04_OKO-03.PROJ) with nine data subdirectories and a tile_index.dat file as shown in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-03/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets.", "creationDate": "2023-11-06T21:49:24.739437", "lastUpdatedDate": "2023-11-06T21:48:29", "latestDataUpdateTime": "2025-03-29T01:54:35", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ForestScan project, GEO-TREES, BIOMASS mission, European Space Agency (ESA), Earth Observation (EO) calibration/validation, Terrestrial LiDAR Scanning (TLS), Unpiloted Aerial Vehicle LiDAR Scanning (UAV-LS), Aerial LiDAR Scanning (ALS), Digital twins, Above Ground Biomass (AGB) estimates, Carbon estimates, Forest structure, Tree segmentation, 3D tree structure, Field protocols", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2025-03-27T15:28:45", "doiPublishedTime": "2025-03-28T16:54:13", "removedDataTime": null, "geographicExtent": { "ob_id": 2374, "bboxName": "TLS - OKO -03 Gabon Ogooué-Ivindo Lopé", "eastBoundLongitude": 11.578, "westBoundLongitude": 11.578, "southBoundLatitude": -0.192, "northBoundLatitude": -0.192 }, "verticalExtent": null, "result_field": { "ob_id": 43568, "dataPath": "/neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-03/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1252104515880, "numberOfFiles": 682452, "fileFormat": "Terrestrial LiDAR scanner data in csv, point cloud and Riegl Proprietary raw data format. Details of formats and data structure can be found in the archived document /neodc/forestscan/data/gabon/lope/TLS_lope_2022/OKO-03/ForestScan_example_data_directory_structure.pdf which details the data structure shared by all FBRSM plot TLS datasets. Further explanation of the files and their origin can also be found in the process computation section." }, "timePeriod": { "ob_id": 12111, "startTime": "2022-07-04T00:00:00", "endTime": "2022-07-10T00:00:00" }, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 43565, "uuid": "c384fdd708e44dc29371d30ba2ace0a7", "short_code": "cmppr", "title": "Terrestrial laser scan composite process for ForestScan Plots in Gabon", "abstract": "Terrestrial laser scan composite process for ForestScan Plots in Gabon" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2528, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 8, "licenceURL": "http://creativecommons.org/licenses/by/4.0/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 40337, "uuid": "3da9f666e3ea4bb3af9cca064f05d3af", "short_code": "proj", "title": "ForestScan Project: a unique multiscale dataset of tropical forest structure across 3 continents including terrestrial, UAV and airborne LiDAR and in-situ forest census data", "abstract": "The ForestScan project was conceived to evaluate new technologies for characterizing forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). It is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and is part of GEO-TREES, an international consortium dedicated to establishing a global network of Forest Biomass Reference Measurement Sites (FBRMS) to support EO and encourage investment in relevant field-based observations and science. ForestScan is the first demonstration of what can be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of E-derived AGB estimates.\r\n\r\nWe present data from the ForestScan project, a unique multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unpiloted aerial vehicle LiDAR scanning (UAV-LS), airborne LiDAR scanning (ALS), and in-situ tree census and ancillary data. These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for three FBRMS: FBRMS-01: Paracou, French Guiana; FBRMS-02: Lopé, Gabon; and FBRMS-03: Kabili-Sepilok, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection. We also provide detailed data collection protocols and recommendations for TLS, UAV-LS, and plot census measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates. We outline the requirements and challenges for field data collection for each data type and discuss the practical considerations for establishing new FBRMS or upgrading existing sites to FBRMS standard, including insights into the associated costs and benefits.\r\n\r\nThe ForestScan Project was funded under ESA contract: 4000126857/20/NL/AI" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 13281 ], "observationcollection_set": [ { "ob_id": 40623, "uuid": "88a8620229014e0ebacf0606b302112d", "short_code": "coll", "title": "ForestScan Collection", "abstract": "This collection is part of the European Space Agency (ESA) funded ForestScan project designed to improve the use of new Earth Observation (EO) estimates of above ground biomass (AGB) by providing TLS-, unmanned airborne vehicles (UAV-LS)- and airborne (ALS) LiDAR scanning-derived AGB and tree census data to compare to allometric and EO-derived estimates. The collection contains a multiscale dataset of tropical forest 3D structural measurements, including terrestrial laser scanning (TLS), unoccupied aerial vehicle LiDAR scanning (UAV-LS), airborne laser scanning (ALS), and in-situ tree census and ancillary data. ForestScan was conceived to evaluate new technologies for characterising forest structure and biomass at Forest Biomass Reference Measurement Sites (FBRMS). These data are critical for the calibration and validation of earth observation (EO) estimates of forest biomass, as well as providing broader insights into tropical forest structure.\r\n\r\nData are presented for the first three Forest Biomass Research Monitoring Sites in Paracou Research Station in French Guiana; Station d'Etudes des Gorilles et Chimpanzes, Lopé National Park in Gabon; and Kabili-Sepilok Forest Reserve, Malaysia. Field data for each site include new 3D LiDAR measurements combined with plot tree census and ancillary data, unmanned aerial vehicle-based laser scanning (UAV-LS) and airborne laser scanning (ALS) where possible, at a multi-hectare scale. Not all data types were collected at all sites, reflecting the practical challenges of field data collection.\r\n\r\nWe also provide detailed field data collection protocols for TLS, UAV-LS, and ALS measurements for each site, along with requirements for ancillary data to enable integration with ALS data (where possible) and upscaling to EO estimates.\r\n\r\nThe ForestScan project is closely aligned with other international initiatives, particularly the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV) AGB cal/val protocols, and GEO-TREES, a new Group on Earth Observations (GEO) initiative aimed at establishing a network of FBRM sites. ForestScan is the first demonstration of what could be achieved more broadly under GEO-TREES, which would significantly expand and enhance the use of EO-derived AGB estimates" }, { "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": [ 198857, 198858, 198859, 198860, 198861, 198862, 208015, 208018, 208016, 208017, 208582, 208583, 208584, 208585, 208586, 208587, 208588, 208589, 208590, 208591, 208592, 208593, 208596, 208597, 208598, 208599, 208601, 208602, 208603, 208604, 208605, 208600, 208613, 208608, 208609, 208615, 208610, 208616, 208617, 208611, 208606, 208595, 208594, 208612, 208614, 208618, 208619 ], "onlineresource_set": [ 94271 ] }, { "ob_id": 40881, "uuid": "5406817a56c347ec8deae69fa1e84dd1", "title": "HadCM3 Climate Simulation - all natural and anthropogenic forcing experiment (Ensemble element 1)", "abstract": "The HadCM3-ALL simulation includes time varying forcing from major and minor greenhouse gases, anthropogenic sulfur cycle with direct and indirect sulphate aerosol effects, and variations in tropospheric and stratospheric ozone based partly on off-line chemistry calculations broadly consistent with the IPCC IS95a emission scenario from 1859 to 2100 as well as changes in total solar irradiance and volcanic aerosol. This dataset is the first ensemble member for the HadCM3-ALL ensemble.\r\n \r\n The HadCM3-ALL experiment was designed to simulate the combined of all natural and anthropogenic forcings supported by the HadCM3 model. The forcings are based upon observations for the period up to 1999 and the IPCC IS95a emission scenario from thereon. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['abwea', 'abweb', 'abwec', 'abwed', 'abzxa']\r\n\r\n Boundary conditions: The HadCM3-ALL simulation includes time varying forcing from major and minor greenhouse gases, anthropogenic sulfur cycle with direct and indirect sulphate aerosol effects, and variations in tropospheric and stratospheric ozone based partly on off-line chemistry calculations broadly consistent with the IPCC IS95a emission scenario from 1859 to 2100 as well as changes in total solar irradiance from Lean, et. al. (1995) and changes in volcanic aerosol from Sato et al. (1993).\r\nReferences: \r\nLean, J., J. Beer, and R. Bradley (1995), Reconstruction of Solar Irradiance Since 1610: Implications for Climate Change. Geophysical Research Letters, 22, 3195-3198.\r\nSato, M., J.E. Hansen, M.P. McCormick, and J.B. Pollack (1995), Stratospheric aerosol optical depths, 1850-1990. Journal of Geophysical Research, 98, 22987-22994.\r\n\r\n\r\n Initial conditions: The first element of the HadCM3-ALL ensemble was initialised with the December 1859 conditions from the HadCM3 control run (HadCM3-ctrl).\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-07T13:09:22.909210", "lastUpdatedDate": "2023-11-07T13:09:22", "latestDataUpdateTime": "2024-09-11T13:10:23", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. Dataset directory structure was reordered by CEDA staff in November 2023 to place experiment runs within a directory of their associated experiment.", "removedDataReason": "", "keywords": "HadCM3-ALL (ensemble element 1), HadCM3, forcings", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "2.75x3.75 degrees in atmosphere 1.25x1.25 degrees in ocean", "status": "completed", "dataPublishedTime": "2024-04-16T08:03:36", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3995, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40882, "dataPath": "/badc/hadcm3/data/HadCM3_ALL/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 268481179607, "numberOfFiles": 11208, "fileFormat": "HadCM3 data is provided in PP (post processing) format." }, "timePeriod": { "ob_id": 11355, "startTime": "1859-12-01T00:00:00", "endTime": "2099-12-01T00:00:00" }, "resultQuality": { "ob_id": 4434, "explanation": "Model data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40883, "uuid": "365ad30b726e4e24ac3e62e9ce6253ee", "short_code": "comp", "title": "Hadley Centre Hadley Centre Coupled Model Version 3", "abstract": "The Hadley Centre Hadley Centre Coupled Model Version 3 was developed from the earlier HadCM2 model in the period 1997-2000. Various improvements were applied to the 19 level atmosphere model and the 20 level ocean model and as a result the model requires no artificial flux adjustments to prevent excessive climate drift. The atmosphere and ocean exchange information once per day, heat and water fluxes being conserved exactly. The main differences from the previous HadCM2 model are a significantly more sophisticated radiation scheme; the inclusion of the direct impact of convection on momentum; and the inclusion of a new land surface scheme that includes a better representation of evaporation, freezing and melting of soil moisture. It improved on the resolution available from previous Hadley Centre models and included support for interactive couplings between the atmosphere and ocean and the biosphere, atmospheric chemistry, the sulphur cycle and atmospheric aerosols. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2613, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "link", "label": "restricted: link group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13847, "uuid": "15b9a832e8964cd89048b0005d3fc9bf", "short_code": "proj", "title": "Met Office Hadley Centre - Modelling", "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": [ 71374, 71375, 71376, 71377, 71378, 71379, 71380, 71381, 71382, 71383, 71384, 71385, 71386, 71387, 71388, 71389, 71390, 71391, 71392, 71393, 71394, 71395, 71396, 71397, 71398, 71399, 71400, 71401, 71402, 71403, 71404, 71405, 71406, 71407, 71408, 71409, 71410, 71411, 71412, 71413, 71414, 71415, 71416, 71417, 71418, 71419, 71420, 71421, 71422, 71423, 71424, 71425, 71426, 71427, 71428, 71429, 71430, 71431, 71432, 71433, 71434, 71435, 71436, 71437, 71438, 71439, 71440, 71441, 71442, 71443, 71444, 71445, 71446, 71447, 71448, 71449, 71450, 71451, 71453, 71454, 71455, 71456, 71457, 71458, 71459, 71460, 71461, 71462, 71463, 71464, 71465, 71466, 71467, 71468, 71469, 71470, 71471, 71472, 71473, 71474, 71475, 71476, 71477, 71478, 71479, 71480, 71481, 71482, 71483, 71484, 71485, 71486, 71487, 71488, 71489, 71490, 71491, 71492, 71493, 72171, 72172, 72180, 72181, 72182, 72183, 72184, 72185, 72186, 72187, 72188, 72189, 72190, 72192, 72193, 72229, 72230, 72231, 72232, 72233, 72234, 72235, 72236, 72237, 72238, 72239, 72240, 72241, 72242, 72243, 72244, 72245, 72246, 72247, 72248, 72249, 72250, 72251, 72252, 72253, 72254, 72255, 72256, 72257, 72258, 72259, 72260, 72261, 72262, 72263, 72264, 72265, 72266, 72267, 72268, 72269, 72270, 72271, 72272, 72273, 72274, 72275, 72276, 72277, 72278, 72279, 72280, 72281, 72282, 72283, 72284, 72285, 72286, 72287, 72288, 72289, 72290, 72291, 72292, 72293, 72294, 72295, 72296, 72297, 72298, 72299, 72300, 72301, 72302, 72303, 72304, 72305, 72306, 72307, 72308, 72309, 72310, 72311, 72312, 72313, 72314, 72315, 72316, 72317, 72318, 72319, 72320, 72321, 72322, 72323, 72324, 72325, 72326, 72327, 72328, 72329 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3791, "uuid": "c5f77b9eade060988ee9b067678aaabc", "short_code": "coll", "title": "Met Office Hadley Centre Coupled Model 3 (HadCM3) data", "abstract": "Numerical model data from various Hadley Centre coupled model 3 (HadCM3) experiments. These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 198863, 198864, 198865, 198866, 198867, 198868, 198869, 198870, 198871 ], "onlineresource_set": [ 85090, 85091, 85092 ] }, { "ob_id": 40884, "uuid": "9eb19fc1b1384fa4b7619e83254d4a96", "title": "HadCM3 Climate Simulation - time varying forcing of major and minor green house gases from 1859-2100", "abstract": "The AFHa forcings simulation contained in this dataset includes time varying forcing from historical concentrations of major and minor greenhouse gases from 1859 to 2100. After 1990 the forcing follows the IPCC IS92a emissions scenario.\r\n \r\n This is an \"all-forcings\" experiment designed to investigate the sensitivity of HadCM3 to various forcings, in this case the forcings are limited to anthropogenic green house gases. It is a partner experiment to HadCM3-AFHb which includes a wider range of anthropogenic forcings. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: aaxze\r\n\r\n Boundary conditions: All anthropogenic forcing from multiple species of greenhouse gases including various minor species specified to represent the IPCC IS92a emissions scenario.\r\n\r\n Initial conditions: Initialised from year 100 of the HadCM3 control run. (i.e. 2091)\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:22:46.810615", "lastUpdatedDate": "2023-11-08T14:22:46", "latestDataUpdateTime": "2024-09-11T13:10:31", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. Dataset directory structure was reordered by CEDA staff in November 2023 to place experiment runs within a directory of their associated experiment.", "removedDataReason": "", "keywords": "HadCM3-AFHa, HadCM3, forcings", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "2.75x3.75 degrees in atmosphere 1.25x1.25 degrees in ocean", "status": "completed", "dataPublishedTime": "2024-04-16T08:07:34", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3996, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40885, "dataPath": "/badc/hadcm3/data/HadCM3_AFHa/", "oldDataPath": [ 3805 ], "storageLocation": "internal", "storageStatus": "online", "volume": 114529145851, "numberOfFiles": 5789, "fileFormat": "HadCM3 data is provided in PP (post processing) format." }, "timePeriod": { "ob_id": 11356, "startTime": "1859-12-01T00:00:00", "endTime": "2100-12-01T00:00:00" }, "resultQuality": { "ob_id": 4435, "explanation": "Model data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40886, "uuid": "2f20786192f34c35b676bf12e8fee01a", "short_code": "comp", "title": "Hadley Centre Hadley Centre Coupled Model Version 3", "abstract": "The Hadley Centre Hadley Centre Coupled Model Version 3 was developed from the earlier HadCM2 model in the period 1997-2000. Various improvements were applied to the 19 level atmosphere model and the 20 level ocean model and as a result the model requires no artificial flux adjustments to prevent excessive climate drift. The atmosphere and ocean exchange information once per day, heat and water fluxes being conserved exactly. The main differences from the previous HadCM2 model are a significantly more sophisticated radiation scheme; the inclusion of the direct impact of convection on momentum; and the inclusion of a new land surface scheme that includes a better representation of evaporation, freezing and melting of soil moisture. It improved on the resolution available from previous Hadley Centre models and included support for interactive couplings between the atmosphere and ocean and the biosphere, atmospheric chemistry, the sulphur cycle and atmospheric aerosols. 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The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['aaxzx', 'aaxzl', 'aaxzz', 'abiaa', 'abqza', 'abqzd', 'abiab', 'abqzb', 'abqze', 'abiac', 'abqzc', 'abqzf']\r\n\r\n Boundary conditions: All anthropogenic forcing from multiple species of greenhouse gases as defined for the IPCC SRESB2 emissions scenario; sulfur (direct and indirect forcing, sulphur chemistry without natural DMS and SO2 background emissions (i.e. anthropogenic SO2 emissions from surface and high level only) and tropospheric/stratospheric ozone.\r\nReferences: \r\nJohns, T.C., J.M. Gregory, W.J. Ingram, C.E. Johnson, A. Jones, J.A. Lowe, J.F.B. Mitchell, D.L. Roberts, D.M.H Sexton, D.S Stevenson, S.F.B. Tett, M.J. Woodage (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics, 20, 583-612.\r\n\r\n Initial conditions: Initialisation for the ensemble was achieved using dump files from previous model runs as follows:\r\n ELEMENT 1: [ run aaxzl - initialised from year 370 of the HadCM3 control run. (i.e. 2361); run aaxzx - initialised from run aaxzl 1849-12-01 ; run aaxzz - initialised from aaxzx 1969-12-01;]\r\n ELEMENT 2: [run abiaa - initialised from run aaxzk 1959-12-01; run abqza - initialised from run abiaa 1959-12-01 ; run abqzd - initialised from run abqza 1974-12-01 ]\r\n ELEMENT 3: [run abiab - initialised from run aaxzk 2059-12-01; run abqzb - initialised from run abiab 1959-12-01 ; run abqze - initialise from run abqzb 1974-12-01 ]\r\n ELEMENT 4: [run abiac - initialised from run aaxzk 2159-12-01; run abqzc - initialised from run abiac 1959-12-01 ; run abqzf - initialised from run abqzc 1974-12-01]\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:23:08.796807", "lastUpdatedDate": "2023-11-08T14:23:08", "latestDataUpdateTime": "2024-09-11T13:10:21", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 198885, 198886, 198887, 198888, 198889, 198890, 198891, 198892, 198893 ], "onlineresource_set": [ 85107, 85108, 85109 ] }, { "ob_id": 40890, "uuid": "4fbb5247fa864210aa0f912f0a10ae0f", "title": "HadCM3 Climate Simulation - time varying forcing of major and minor green house gases, anthropogenic sulfur cycle, sulphate aerosols and tropospheric ozone from 1859-2100", "abstract": "The AFHb forcings simulation contained in this dataset includes time varying forcing from major and minor greenhouse gases, anthropogenic sulphur cycle with direct plus indirect sulphate aerosol effects, and variations in tropospheric ozone based partly on off-line chemistry calculations from 1859 to 2100. After 1990 the forcing follows the IPCC IS92a emissions scenario.\r\n \r\n This is an \"all-forcings\" experiment designed to investigate the sensitivity of HadCM3 to various forcings, including various minor species specified to give IS92a-like forcing variations); sulphate aerosol direct and indirect forcing (via calibrated delta-albedo); sulfur chemistry without natural DMS and 3D SO2 background emissions, (ie. anthropogenic SO2 emissions surface and high level only) and tropospheric/stratospheric ozone. It is a partner experiment to HadCM3-AFHa which is limited to well mixed green house gas forcings. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: aaxzl\r\n\r\n Boundary conditions: HADCM3 all-anthropogenic forcings experiment with greenhouse gas forcing as for HadCM3-AFHa (multiple species of greenhouse gases including various minor species specified to give IS92a-like forcing variations); sulphate aerosol direct and indirect forcing (via calibrated delta-albedo); sulfur chemistry without natural DMS and 3D SO2 background emissions, (ie. anthropogenic SO2 emissions surface and high level only) and tropospheric/stratospheric ozone.\r\n\r\n Initial conditions: Initialised from year 370 of the HadCM3 control run. (i.e. 2361).\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:23:25.255076", "lastUpdatedDate": "2023-11-08T14:23:25", "latestDataUpdateTime": "2024-09-11T13:10:23", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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The experiment was run as a four element ensemble with starting conditions established from the HadCM3 control run at 1859, 1959, 2059 and 2159.\r\n \r\n The Anthropogenic Greenhouse Gas Historical simulation includes forcing from multiple species of greenhouse gases including various minor species specified to give IS92a-like forcing variations. The experiment was run as a four element ensemble with starting conditions established from the HadCM3 control run at 1859, 1959, 2059 and 2159. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['abmba', 'abmbb', 'abmbc', 'abmbd']\r\n\r\n Boundary conditions: Forcing from major and minor greenhouse gas emissions to represent the IS92a emissions scenario.\r\n\r\n Initial conditions: The model is initialised from the control run with a different year for each of the four ensemble elements.\r\n[ABMBA initialised with 1859 conditions from control]\r\n[ABMBB initialised with 1959 conditions from control]\r\n[ABMBC initialised with 2059 conditions from control]\r\n[ABMBD initialised with 2159 conditions from control]\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:30:38.555199", "lastUpdatedDate": "2023-11-08T14:30:38", "latestDataUpdateTime": "2024-09-11T13:10:24", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 198940, 198941, 198942, 198943, 198944, 198945, 198946, 198947, 198948 ], "onlineresource_set": [ 85137, 85138, 85139 ] }, { "ob_id": 40906, "uuid": "889d861aa0c44fb3aef671a9d57313df", "title": "HadCM3 Climate Simulation - IPCC Special Report Emission Scenario (SRES-A1B) with trace gases, ozone, sulphur emission and aerosol forcing all held constant at year 2000 levels.", "abstract": "The SRESA1B-2000S simulation is a parallel simulation to the standard SRESA1B simulation, but in this case the forcings of green house gases (including methane), sulfur (direct and indirect forcing, sulphur chemistry without natural DMS and SO2 background emissions; anthropogenic SO2 emissions from surface and high level only) and tropospheric/stratospheric ozone are held constant at year 2000 levels throughout the simulation.\r\n \r\n This experiment produced model outputs reflecting the SRES-A2 emissions scenario . The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: acfxg\r\n\r\n Boundary conditions: All anthropogenic forcing from multiple species of greenhouse gases as defined for the IPCC SRESA1B emissions scenario, sulfur direct and indirect forcing (sulfur chemistry without natural DMS and SO2 background emissions; anthropogenic SO2 emissions from surface and high level only) and tropospheric/stratospheric ozone are held constant at year 2000 levels throughout the simulation.\r\n\r\n Initial conditions: This experiment was initialised using one of the HadCM3 Historic Anthropogenic Forcing run ensemble elements (run: abqzd - 1999-12-01).\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:39:50.110635", "lastUpdatedDate": "2023-11-08T14:39:50", "latestDataUpdateTime": "2024-09-11T13:10:30", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. Dataset directory structure was reordered by CEDA staff in November 2023 to place experiment runs within a directory of their associated experiment.", "removedDataReason": "", "keywords": "HadCM3-SRESA1B-2000S, HadCM3, forcings, IPCC, SRESA1B", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "2.75x3.75 degrees in atmosphere 1.25x1.25 degrees in ocean", "status": "completed", "dataPublishedTime": "2024-04-16T08:14:50", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4003, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40907, "dataPath": "/badc/hadcm3/data/HadCM3_SRESA1B-2000S/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 80311383359, "numberOfFiles": 2401, "fileFormat": "HadCM3 data is provided in PP (post processing) format." }, "timePeriod": { "ob_id": 11363, "startTime": "1999-12-01T00:00:00", "endTime": "2099-12-01T00:00:00" }, "resultQuality": { "ob_id": 4442, "explanation": "Model data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40908, "uuid": "35fa4ae7d506422fb00d865481d8e72e", "short_code": "comp", "title": "Hadley Centre Hadley Centre Coupled Model Version 3", "abstract": "The Hadley Centre Hadley Centre Coupled Model Version 3 was developed from the earlier HadCM2 model in the period 1997-2000. Various improvements were applied to the 19 level atmosphere model and the 20 level ocean model and as a result the model requires no artificial flux adjustments to prevent excessive climate drift. The atmosphere and ocean exchange information once per day, heat and water fluxes being conserved exactly. The main differences from the previous HadCM2 model are a significantly more sophisticated radiation scheme; the inclusion of the direct impact of convection on momentum; and the inclusion of a new land surface scheme that includes a better representation of evaporation, freezing and melting of soil moisture. It improved on the resolution available from previous Hadley Centre models and included support for interactive couplings between the atmosphere and ocean and the biosphere, atmospheric chemistry, the sulphur cycle and atmospheric aerosols. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2613, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "link", "label": "restricted: link group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13847, "uuid": "15b9a832e8964cd89048b0005d3fc9bf", "short_code": "proj", "title": "Met Office Hadley Centre - Modelling", "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": [ 71374, 71375, 71376, 71377, 71378, 71379, 71380, 71381, 71382, 71383, 71384, 71385, 71386, 71387, 71388, 71389, 71390, 71391, 71392, 71393, 71394, 71395, 71396, 71397, 71398, 71399, 71400, 71401, 71402, 71403, 71404, 71405, 71406, 71407, 71408, 71409, 71410, 71411, 71412, 71413, 71414, 71415, 71416, 71417, 71418, 71419, 71420, 71421, 71422, 71423, 71424, 71425, 71426, 71427, 71428, 71429, 71430, 71431, 71432, 71433, 71434, 71435, 71436, 71437, 71438, 71439, 71440, 71441, 71442, 71443, 71444, 71445, 71446, 71447, 71448, 71449, 71450, 71451, 71452, 71453, 71454, 71455, 71456, 71457, 71458, 71459, 71460, 71461, 71462, 71463, 71464, 71465, 71466, 71467, 71468, 71469, 71470, 71471, 71472, 71473, 71474, 71475, 71476, 71477, 71478, 71479, 71480, 71481, 71482, 71483, 71484, 71485, 71486, 71487, 71488, 71489, 71490, 71491, 71492, 71493 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3791, "uuid": "c5f77b9eade060988ee9b067678aaabc", "short_code": "coll", "title": "Met Office Hadley Centre Coupled Model 3 (HadCM3) data", "abstract": "Numerical model data from various Hadley Centre coupled model 3 (HadCM3) experiments. These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 198951, 198952, 198953, 198954, 198955, 198956, 198957, 198958, 198959 ], "onlineresource_set": [ 85143, 85144, 85145 ] }, { "ob_id": 40916, "uuid": "0c412a84a18945d29e708f8f55edab80", "title": "HadCM3 historical emissions simulation generated for the QUMP (Quantifying Uncertainty in Model Predictions) Project.", "abstract": "The historical emissions simulation contained in this dataset includes time varying (1949-1989) forcing from major and minor greenhouse gases, anthropogenic sulphur cycle with direct plus indirect sulphate aerosol effects, and variations in tropospheric ozone based partly on off-line chemistry calculations. It forms part of the second QUMP (Quantifying Uncertainty in Model Predictions) fully coupled transient ensemble. A previous simulation covered the period 1859-1949 (HadCM3-HIST1-QUMP).\r\n \r\n The simulation data contained in this dataset is part of the 2nd QUMP (Quantifying Uncertainty in Model Predictions) Fully Coupled Transient Ensemble. It uses boundary conditions representing historical emissions covering the latter part of the 20th century. This run is part of a 17 element historical emissions ensemble produced by the Hadley Centre QUMP project. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: aenwh\r\n\r\n Boundary conditions: HadCM3 all-anthropogenic forcings experiment with multiple species of greenhouse gases including various minor species); sulphate aerosol direct and indirect forcing; sulfur chemistry with natural DMS and 3D SO2 background emissions, and tropospheric/stratospheric ozone.\r\n\r\n Initial conditions: The model is initialised directly from the previous QUMP historical emissions simulation covering the period 1859-1949 (run: aenwg date: 1949-12-01).\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.\r\n\r\n\r\nList of climate variables:\r\nSTREAMFUNCTION (OCEAN) CM3/S\r\nHSNOW: AGGREGATE LOCAL SNOW DEPTH M\r\nAICE: AGGREGATE ICE CONCENTRATION\r\nHICE: AGGREGATE GBM ICE DEPTH M\r\nU WIND ON PRESSURE LEVELS B GRID\r\nV WIND ON PRESSURE LEVELS B GRID\r\nGEOPOTENTIAL HEIGHT: PRESSURE LEVELS\r\nTEMPERATURE ON PRESSURE LEVELS\r\nPRESSURE AT MEAN SEA LEVEL\r\nU COMPNT OF WIND AFTER TIMESTEP\r\nV COMPNT OF WIND AFTER TIMESTEP\r\nTHETA AFTER TIMESTEP\r\nSPECIFIC HUMIDITY AFTER TIMESTEP\r\nCONV CLOUD AMOUNT AFTER TIMESTEP\r\nCONV CLOUD LIQUID WATER PATH\r\nSNOW AMOUNT OVER LAND AFT TSTP KG/M2\r\nSURFACE TEMPERATURE AFTER TIMESTEP\r\nBOUNDARY LAYER DEPTH AFTER TIMESTEP\r\nSURFACE ZONAL CURRENT AFTER TIMESTEP\r\nSURFACE MERID CURRENT AFTER TIMESTEP\r\nFRAC OF SEA ICE IN SEA AFTER TSTEP\r\nSEA ICE DEPTH (MEAN OVER ICE) M\r\nDIMETHYL SULPHIDE EMISSIONS\r\nSO2 MASS MIXING RATIO AFTER TSTEP\r\nPOTENTIAL TEMPERATURE (OCEAN) DEG.C\r\nDIMETHYL SULPHIDE MIX RAT AFTER TS\r\nSALINITY (OCEAN) (PSU-35)/1000\r\nSO4 AITKEN MODE AEROSOL AFTER TSTEP\r\nSO4 ACCUM. MODE AEROSOL AFTER TSTEP\r\nSO4 DISSOLVED AEROSOL AFTER TSTEP\r\nBAROCLINIC U_VELOCITY (OCEAN) CM/S\r\nBAROCLINIC V_VELOCITY (OCEAN) CM/S\r\nSTREAMFUNCTION (OCEAN) CM3/S\r\nSTREAMFN TENDENCY (OCEAN) CM3/S/TS\r\nMIXED LAYER DEPTH (OCEAN) M\r\nHSNOW: AGGREGATE LOCAL SNOW DEPTH M\r\nGBM CARYHEAT MISC HEAT FLX(ICE) W/M2\r\nGBM HEAT FLUX:OCEAN TO ICE(OCN) W/M2\r\nRATE OF SALINITY CHANGE (ICE) PSU/S\r\nAICE: AGGREGATE ICE CONCENTRATION\r\nHICE: AGGREGATE GBM ICE DEPTH M\r\nTAUX: X_WINDSTRESS N/M2 A\r\nTAUY: Y_WINDSTRESS N/M2 A\r\nWME: WIND MIXING ENERGY FLUX W/M2 A\r\nSOL: PEN.SOLAR*LF INTO OCEAN W/M2 A\r\nHTN:NONPEN.HT.FLX*LF INTO OCN W/M2 A\r\nPLE:PRECIP-EVAP INTO OCEAN KG/M2/S A\r\nRIVER OUTFLOW INTO OCEAN KG/M2/S A\r\nSNOWFALL INTO OCN/ONTO ICE KG/M2/S A\r\nSUBLIMATION FROM SEAICE KG/M2/S A\r\nP-E FLUX CORRECTION KG/M2/S A\r\nTOPMELT: GBM SEAICE HEAT FLUX W/M2 A\r\nBOTMELT: GBM SEAICE HEAT FLUX W/M2 A\r\nTHICKNESS DIFF COEFF (OCEAN) CM2/S\r\nNET DOWN SURFACE SW FLUX: SW TS ONLY\r\nNET DN SW RAD FLUX:OPEN SEA:SEA MEAN\r\nNET DOWN SURFACE SW FLUX BELOW 690NM\r\nINCOMING SW RAD FLUX (TOA): ALL TSS\r\nOUTGOING SW RAD FLUX (TOA)\r\nCLEAR-SKY (II) UPWARD SW FLUX (TOA)\r\nCLEAR-SKY (II) DOWN SURFACE SW FLUX\r\nCLEAR-SKY (II) UP SURFACE SW FLUX\r\nLAYER CLD LIQ RE * LAYER CLD WEIGHT\r\nLAYER CLOUD WEIGHT FOR MICROPHYSICS\r\nLAYER CLD LIQUID WATER PATH * WEIGHT\r\nCONV CLOUD LIQ RE * CONV CLD WEIGHT\r\nCONV CLOUD WEIGHT FOR MICROPHYSICS\r\nSW HEATING RATES: ALL TIMESTEPS\r\nCLEAR-SKY SW HEATING RATES\r\nTOTAL DOWNWARD SURFACE SW FLUX\r\nNET DOWNWARD SW FLUX AT THE TROP.\r\nUPWARD SW FLUX AT THE TROP.\r\nDROPLET NUMBER CONC * LYR CLOUD WGT\r\nLAYER CLOUD LWC * LAYER CLOUD WEIGHT\r\nSO4 CCN KG/M3 * COND SAMPLING WEIGHT\r\nCONDITIONAL SAMPLING WEIGHT\r\n2-D RE DISTRIBUTION * 2-D RE WEIGHT\r\nWEIGHT FOR 2-D RE DISTRIBUTION\r\nWEIGHTED SW CLOUD EXTINCTION\r\nWEIGHTS FOR CLOUD SW EXTINCTION\r\nWEIGHTED SW LAYER CLOUD EXTINCTION\r\nWEIGHTS FOR LAYER CLD SW EXTINCTION\r\nNET DOWN SURFACE LW RAD FLUX\r\nNET DN LW RAD FLUX:OPEN SEA:SEA MEAN\r\nTOTAL CLOUD AMOUNT IN LW RADIATION\r\nOUTGOING LW RAD FLUX (TOA)\r\nCLEAR-SKY (II) UPWARD LW FLUX (TOA)\r\nDOWNWARD LW RAD FLUX: SURFACE\r\nCLEAR-SKY (II) DOWN SURFACE LW FLUX\r\nLW HEATING RATES\r\nCLEAR-SKY LW HEATING RATES\r\nNET DOWNWARD LW FLUX AT THE TROP.\r\nTOTAL DOWNWARD LW FLUX AT THE TROP.\r\nTOTAL CLOUD AMOUNT ON LEVELS\r\nWEIGHTED CLOUD ABSORPTIVITY\r\nWEIGHTS FOR CLOUD ABSORPTIVITY\r\nWEIGHTED LAYER CLOUD ABSORPTIVITY\r\nWEIGHTS FOR LAYER CLD ABSORPTIVITY\r\nISCCP CLOUD WEIGHTS\r\nISCCP CLOUD 0.3 <= tau\r\nISCCP CLOUD tau < 0.3\r\nISCCP CLOUD 0.3 <= tau < 1.3\r\nISCCP CLOUD 1.3 <= tau < 3.6\r\nISCCP CLOUD 3.6 <= tau < 9.4\r\nISCCP CLOUD 9.4 <= tau < 23.0\r\nISCCP CLOUD 23.0 <= tau < 60.0\r\nISCCP CLOUD 60.0 <= tau\r\nHT FLUX THROUGH SEAICE:SEA MEAN W/M2\r\nHT FLUX FROM SURF TO DEEP SOIL LEV 1\r\nSURFACE HEAT FLUX W/M2\r\nX-COMP OF SURF & BL WIND STRESS N/M2\r\nY-COMP OF SURF & BL WIND STRESS N/M2\r\nSURFACE TOTAL MOISTURE FLUX KG/M2/S\r\nWIND MIX EN'GY FL TO SEA:SEA MN W/M2\r\n10 METRE WIND U-COMP B GRID\r\n10 METRE WIND V-COMP B GRID\r\nSFC SH FLX FROM OPEN SEA:SEA MN W/M2\r\nEVAP FROM SOIL SURF -AMOUNT KG/M2/TS\r\nSUBLIM. FROM SURFACE (GBM) KG/M2/TS\r\nEVAP FROM OPEN SEA: SEA MEAN KG/M2/S\r\nSURFACE LATENT HEAT FLUX W/M2\r\nSEAICE TOP MELT LH FLX:SEA MEAN W/M2\r\nTEMPERATURE AT 1.5M\r\nSPECIFIC HUMIDITY AT 1.5M\r\nDEEP SOIL TEMPERATURE AFTER B.LAYER\r\nRELATIVE HUMIDITY AT 1.5M\r\nSURFACE SNOWMELT HEAT FLUX W/M2\r\nCANOPY CONDUCTANCE M/S\r\nGROSS PRIMARY PRODUCTIVITY KG C/M2/S\r\nNET PRIMARY PRODUCTIVITY KG C/M2/S\r\nPLANT RESPIRATION KG/M2/S\r\nSO2 SURFACE DRY DEP FLUX KG/M2/S\r\nSO4 AIT SURF DRY DEP FLUX KG/M2/S\r\nSO4 ACC SURF DRY DEP FLUX KG/M2/S\r\nSO4 DIS SURF DRY DEP FLUX KG/M2/S\r\nSURFACE NET RADIATION ON TILES\r\nLARGE SCALE RAINFALL RATE KG/M2/S\r\nLARGE SCALE SNOWFALL RATE KG/M2/S\r\nSO2 SCAVENGED BY LS PPN KG/M2/S\r\nSO4 DIS SCAVNGD BY LS PPN KG/M2/S\r\nCONVECTIVE RAINFALL RATE KG/M2/S\r\nCONVECTIVE SNOWFALL RATE KG/M2/S\r\nPRESSURE AT CONVECTIVE CLOUD BASE\r\nPRESSURE AT CONVECTIVE CLOUD TOP\r\nCONV. CLOUD AMOUNT ON EACH MODEL LEV\r\nTOTAL RAINFALL RATE: LS+CONV KG/M2/S\r\nTOTAL SNOWFALL RATE: LS+CONV KG/M2/S\r\nTOTAL PRECIPITATION RATE KG/M2/S\r\nSO2 SCAVENGED BY CONV PPN KG/M2/SEC\r\nSO4 AIT SCAVNGD BY CONV PPN KG/M2/S\r\nSO4 ACC SCAVNGD BY CONV PPN KG/M2/S\r\nSO4 DIS SCAVNGD BY CONV PPN KG/M2/S\r\nX COMPONENT OF GRAVITY WAVE STRESS\r\nY COMPONENT OF GRAVITY WAVE STRESS\r\nSTANDARD DEVIATION OF OROGRAPHY\r\nSNOW MASS AFTER HYDROLOGY KG/M2\r\nLAND SNOW MELT AMOUNT KG/M2/TS\r\nLAND SNOW MELT HEAT FLUX W/M2\r\nSFC RUNOFF AMOUNT:LAND MEAN KG/M2/TS\r\nSUB-SFC RUNOFF AMT:LAND MN KG/M2/TS\r\nSOIL MOISTURE CONTENT\r\nCANOPY WATER CONTENT\r\nSOIL MOISTURE CONTENT IN A LAYER\r\nDEEP SOIL TEMP. AFTER HYDROLOGY DEGK\r\nUNFROZEN SOIL MOISTURE FRACTION\r\nFROZEN SOIL MOISTURE FRACTION\r\nLAND SNOW MELT RATE KG/M2/S\r\nSURFACE RUNOFF RATE KG/M2/S\r\nSUB-SURFACE RUNOFF RATE KG/M2/S\r\nBULK CLOUD AMOUNT AFTER MAIN CLOUD\r\nCLOUD LIQUID WATER AFTER MAIN CLOUD\r\nCLOUD ICE CONTENT AFTER DYNAM CLOUD\r\nTOTAL CLOUD AMOUNT MAX/RANDOM OVERLP\r\nATMOS ENERGY CORR'N IN COLUMN W/M2\r\nU WIND ON PRESSURE LEVELS B GRID\r\nV WIND ON PRESSURE LEVELS B GRID\r\nTHETA ON PV=+/-2 SURFACE\r\nTHETA AT PV POINTS\r\nPV ON MODEL LEVELS(CALC PV)\r\nGEOPOTENTIAL HEIGHT: PRESSURE LEVELS\r\nTEMPERATURE ON PRESSURE LEVELS\r\nRELATIVE HUMIDITY WRT ICE ON P LVS\r\nPRESSURE AT MEAN SEA LEVEL\r\nMSA MASS MIXING RATIO FLUX KG/KG/S\r\nVERT.VEL. ON OCEAN HALF LEVELS CM/S\r\nGBM HTN INTO OCEAN BUDGET W/M**2\r\nSNOWRATE WHERE NO ICE KG M**-2 S**-1\r\nCARYHEAT AFTER ROW CALCULATION W/M2\r\nMEAD DIAGNOSTICS: TEMPERATURE W\r\nMEAD DIAGNOSTICS: SALINITY KG/S\r\nBAROCLINIC X-ACCN (ZUN) CM/S**2\r\nBAROCLINIC Y-ACCN (ZVN) CM/S**2\r\nANOM. HEAT \"SINK\" AT OCN FLOOR W/M2\r\nWATER_FLUX*SALINITY/DENSITY m Gs**-1\r\nGM EDDY U VELOCITY (OCEAN)\r\nGM EDDY V VELOCITY (N FACE) (OCEAN)\r\nGM EDDY W VEL (TOP FACE) (OCEAN)\r\nDTHETA/DT FROM G&MCW SCHEME K/Gs\r\nTOTAL OCEAN U-VELOCITY CM S**-1\r\nTOTAL OCEAN V-VELOCITY CM S**-1\r\nDS/DT FROM G&MCW SCHEME Gs**-1\r\nAICE INC. DUE TO ADVECTION FRACT/TS\r\nHICE INC. DUE TO ADV (& DIFF) M/TS\r\nGBM SNOWDEPTH INC ADVECTION M/TS\r\nHICE INC. DUE TO DIFFUSION M/TS\r\nU COMPONENT OF ICE VELOCITY (M.S-1)\r\nV COMPONENT OF ICE VELOCITY (M.S-1)\r\nAICE INC. (THERMODYNAMIC) FRACT/TS\r\nHICE INC. 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This dataset is the corrected version of this experiment - a previous run (HadCM3-NFVSorig) had incorrectly introduced the volcanic aerosol and solar radiation spectrum.\r\n \r\n The Natural Forcing (Volcanic and Solar) simulation contained in this dataset copied the control run (HadCM3-ctrl) with the addition of natural forcing from volcanic and solar sources. This dataset is the corrected version of this experiment - a previous run (HadCM3-NFVSorig) had incorrectly introduced the volcanic aerosol and solar radiation spectrum. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['abvwa', 'abvwb', 'abvwc', 'abvwd']\r\n\r\n Boundary conditions: Essentially a repeat of the HadCM3 control run but including natural forcings from volcano and solar sources. The volcanic forcing data are taken from Sato et al. (1993) reconstruction of stratospheric volcanic aerosol optical depth at .55 microns. The solar forcing data was taken from the reconstruction of Lean et al. (1995).\r\nReferences: \r\nSato, M., J.E. Hansen, M.P. McCormick, and J.B. Pollack (1993) Stratospheric aerosol optical depth, 1850-1990. Journal of Geophysical Research, 98, 22987-22994..\r\nLean, J., J. Beer, and R. Bradley (1995), Reconstruction of Solar Irradiance Since 1610: Implications for Climate Change. 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This dataset is the original version of this experiment that incorrectly introduced the volcanic aerosol and solar radiation spectrum - a subsequent run (HadCM3-NFVScorr) corrected the error.\r\n \r\n The Natural Forcing (Volcanic and Solar) simulation contained in this dataset copied the control run (HadCM3-ctrl) with the addition of natural forcing from volcanic and solar sources. This dataset is the original version of this experiment that incorrectly introduced the volcanic aerosol and solar radiation spectrum - a subsequent run (HadCM3-NFVScorr) corrected the error. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['abicd', 'abice', 'abicf', 'abicg']\r\n\r\n Boundary conditions: Essentially a repeat of the HadCM3 control run but including natural forcings from volcano and solar sources. The volcanic forcing data are taken from Sato et al. (1993) reconstruction of stratospheric volcanic aerosol optical depth at .55 microns. The solar forcing data was taken from the reconstruction of Lean et al. (1995).\r\nReferences: \r\nSato, M., J.E. Hansen, M.P. McCormick, and J.B. Pollack (1993) Stratospheric aerosol optical depth, 1850-1990. Journal of Geophysical Research, 98, 22987-22994..\r\nLean, J., J. Beer, and R. Bradley (1995), Reconstruction of Solar Irradiance Since 1610: Implications for Climate Change. Geophysical Research Letters, 22, 3195-3198.\r\n\r\n\r\n Initial conditions: The model is initialised from the control run with a different year for each of the four ensemble elements.\r\n[ABICD initialised with 1859 conditions from control]\r\n[ABICE initialised with 1959 conditions from control]\r\n[ABICF initialised with 2059 conditions from control]\r\n[ABICG initialised with 2159 conditions from control]\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:43:22.496996", "lastUpdatedDate": "2023-11-08T14:43:22", "latestDataUpdateTime": "2024-09-11T13:10:34", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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The Hadley Centre climate projections were the basis for the Stern Review on the Economics of Climate Change." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 71374, 71375, 71376, 71377, 71378, 71379, 71380, 71381, 71382, 71383, 71384, 71385, 71386, 71387, 71388, 71389, 71390, 71391, 71392, 71393, 71394, 71395, 71396, 71397, 71398, 71399, 71400, 71401, 71402, 71403, 71404, 71405, 71406, 71407, 71408, 71409, 71410, 71411, 71412, 71413, 71414, 71415, 71416, 71417, 71418, 71419, 71420, 71421, 71422, 71423, 71424, 71425, 71426, 71427, 71428, 71429, 71430, 71431, 71432, 71433, 71434, 71435, 71436, 71437, 71438, 71439, 71440, 71441, 71442, 71443, 71444, 71445, 71446, 71447, 71448, 71449, 71450, 71451, 71452, 71453, 71454, 71455, 71456, 71457, 71458, 71459, 71460, 71461, 71462, 71463, 71464, 71465, 71466, 71467, 71468, 71469, 71470, 71471, 71472, 71473, 71474, 71475, 71476, 71477, 71478, 71479, 71480, 71481, 71482, 71483, 71484, 71485, 71486, 71487, 71488, 71489, 71490, 71491, 71492, 71493 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3791, "uuid": "c5f77b9eade060988ee9b067678aaabc", "short_code": "coll", "title": "Met Office Hadley Centre Coupled Model 3 (HadCM3) data", "abstract": "Numerical model data from various Hadley Centre coupled model 3 (HadCM3) experiments. These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 199083, 199084, 199085, 199086, 199087, 199088, 199089, 199090, 199091 ], "onlineresource_set": [ 85215, 85216, 85217 ] }, { "ob_id": 40947, "uuid": "49e679b0ea5340578bdfa5e985817b69", "title": "HadCM3 Climate Simulation - IPCC Special Report Emission Scenario (SRES-B2)", "abstract": "The SRESB2 ensemble simulation contained in this dataset includes forcings of green house gases (including methane) that are consistent with historical levels and the future IPCC SRESB2 scenario, sulfur (direct and indirect forcing, sulphur chemistry without natural DMS and SO2 background emissions; anthropogenic SO2 emissions from surface and high level only) and tropospheric/stratospheric ozone.\r\n \r\n This experiment produced model outputs reflecting the SRES-B2 emissions scenario . The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['aaxzx', 'aaxzz', 'abwaa']\r\n\r\n Boundary conditions: All anthropogenic forcing from multiple species of greenhouse gases as defined for the IPCC SRESB2 emissions scenario, sulfur (direct and indirect forcing, sulphur chemistry without natural DMS and SO2 background emissions; anthropogenic SO2 emissions from surface and high level only) and tropospheric/stratospheric ozone.\r\n\r\n Initial conditions: Element 1 (run: aaxzz) used run: aaxzx (date - 1969-12-01) for initialisation. Element 2 (run: abwaa) used run: abqzd (date - 1989-12-01) for initialisation. Both initialisation runs are from the historic anthropogenic forcing ensemble runs of HadCM3.\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:45:13.024187", "lastUpdatedDate": "2023-11-08T14:45:13", "latestDataUpdateTime": "2024-09-11T13:10:22", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. Dataset directory structure was reordered by CEDA staff in November 2023 to place experiment runs within a directory of their associated experiment.", "removedDataReason": "", "keywords": "HadCM3-SRESB2, HadCM3, forcings, IPCC, SRESB2", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "2.75x3.75 degrees in atmosphere 1.25x1.25 degrees in ocean", "status": "completed", "dataPublishedTime": "2024-01-15T15:44:04", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4016, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40948, "dataPath": "/badc/hadcm3/data/HadCM3_SRESB2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 69124970033, "numberOfFiles": 3999, "fileFormat": "HadCM3 data is provided in PP (post processing) format." }, "timePeriod": { "ob_id": 11376, "startTime": "1989-12-01T00:00:00", "endTime": "2100-12-01T00:00:00" }, "resultQuality": { "ob_id": 4455, "explanation": "Model data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40949, "uuid": "e85f9e505d634d2b81c595567bb26c6a", "short_code": "comp", "title": "Hadley Centre Hadley Centre Coupled Model Version 3", "abstract": "The Hadley Centre Hadley Centre Coupled Model Version 3 was developed from the earlier HadCM2 model in the period 1997-2000. Various improvements were applied to the 19 level atmosphere model and the 20 level ocean model and as a result the model requires no artificial flux adjustments to prevent excessive climate drift. The atmosphere and ocean exchange information once per day, heat and water fluxes being conserved exactly. The main differences from the previous HadCM2 model are a significantly more sophisticated radiation scheme; the inclusion of the direct impact of convection on momentum; and the inclusion of a new land surface scheme that includes a better representation of evaporation, freezing and melting of soil moisture. It improved on the resolution available from previous Hadley Centre models and included support for interactive couplings between the atmosphere and ocean and the biosphere, atmospheric chemistry, the sulphur cycle and atmospheric aerosols. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2613, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "link", "label": "restricted: link group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13847, "uuid": "15b9a832e8964cd89048b0005d3fc9bf", "short_code": "proj", "title": "Met Office Hadley Centre - Modelling", "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": [ 71374, 71375, 71376, 71377, 71378, 71379, 71380, 71381, 71382, 71383, 71384, 71385, 71386, 71387, 71388, 71389, 71390, 71391, 71392, 71393, 71394, 71395, 71396, 71397, 71398, 71399, 71400, 71401, 71402, 71403, 71404, 71405, 71406, 71407, 71408, 71409, 71410, 71411, 71412, 71413, 71414, 71415, 71416, 71417, 71418, 71419, 71420, 71421, 71422, 71423, 71424, 71425, 71426, 71427, 71428, 71429, 71430, 71431, 71432, 71433, 71434, 71435, 71436, 71437, 71438, 71439, 71440, 71441, 71442, 71443, 71444, 71445, 71446, 71447, 71448, 71449, 71450, 71451, 71452, 71453, 71454, 71455, 71456, 71457, 71458, 71459, 71460, 71461, 71462, 71463, 71464, 71465, 71466, 71467, 71468, 71469, 71470, 71471, 71472, 71473, 71474, 71475, 71476, 71477, 71478, 71479, 71480, 71481, 71482, 71483, 71484, 71485, 71486, 71487, 71488, 71489, 71490, 71491, 71492, 71493, 72171, 72172 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3791, "uuid": "c5f77b9eade060988ee9b067678aaabc", "short_code": "coll", "title": "Met Office Hadley Centre Coupled Model 3 (HadCM3) data", "abstract": "Numerical model data from various Hadley Centre coupled model 3 (HadCM3) experiments. These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 199094, 199095, 199096, 199097, 199098, 199099, 199100, 199101, 199102 ], "onlineresource_set": [ 85221, 85222, 85223 ] }, { "ob_id": 40950, "uuid": "0240963d5ca74580821af97b30a67c7f", "title": "HadCM3 Climate Simulation - suppressed thermohaline circulation experiment.", "abstract": "The Thermohaline Circulation simulation dataset was produced to investigate the response of the HadCM3 model to a suppression of the thermohaline circulation in the Atlantic Ocean. The suppression was induced by a strong initial perturbation to the salinity distribution in the upper layer of the northern North Atlantic. The model was then allowed to adjust freely.\r\n \r\n The Thermohaline Circulation simulation data contained in this dataset were used to investigate the response of HadCM3 to a significant increase in the freshwater influx to the ocean thus weaking the thermohaline circulation. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: abpsp\r\n\r\n Boundary conditions: Fixed forcing representative of late nineteenth century conditions as per the HadCM3 control run.\r\nReferences: \r\nVellinga, M., R.A. Wood and J.M. Gregory (2002) Processes governing the recovery of a perturbed thermohaline circulation in HadCM3. Journal of Climate, Vol 15, 764-780.\r\nJohns, T.C., J.M. Gregory, W.J. Ingram, C.E. Johnson, A. Jones, J.A. Lowe, J.F.B. Mitchell, D.L. Roberts, D.M.H Sexton, D.S Stevenson, S.F.B. Tett, M.J. Woodage (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics, pp583-612.\r\n\r\n\r\n Initial conditions: The model was initialised using the conditions from 1991 of the HadCM3 control run. These conditions were perturbed by producing a weakened thermohaline circulation in the model. This was achieved by replacing the salinity field in the top 800m of the northern North Atlantic [(50-90 degrees N) x (80 degrees W to 20 degrees E)] with a vertical profile that is much fresher and has a deeper pycnocline. On average the water in the area of the perturbation is made 2 PSU fresher. Assuming a reference salinity of 35 PSU, the area would have to receive a freshwater pulse of about 16 Sv yr to experience this freshening. Conservation of salt was assured by globally redistributing the salt taken out of the North Atlantic, increasing salinity everywhere by about 0.01 PSU. The model was allowed to adjust freely to the new salinity field.\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:45:21.430827", "lastUpdatedDate": "2023-11-08T14:45:21", "latestDataUpdateTime": "2024-09-11T13:10:20", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. Dataset directory structure was reordered by CEDA staff in November 2023 to place experiment runs within a directory of their associated experiment.", "removedDataReason": "", "keywords": "HadCM3-THC, Thermohaline Circulation, HadCM3, forcings", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "2.75x3.75 degrees in atmosphere 1.25x1.25 degrees in ocean", "status": "completed", "dataPublishedTime": "2024-04-16T08:08:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4017, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 40951, "dataPath": "/badc/hadcm3/data/HadCM3_THC/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 271680429193, "numberOfFiles": 9625, "fileFormat": "HadCM3 data is provided in PP (post processing) format." }, "timePeriod": { "ob_id": 11377, "startTime": "2091-12-01T00:00:00", "endTime": "2257-05-01T00:00:00" }, "resultQuality": { "ob_id": 4456, "explanation": "Model data.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2023-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 40952, "uuid": "ac49c510137c4e22a222de25e526c6d8", "short_code": "comp", "title": "Hadley Centre Hadley Centre Coupled Model Version 3", "abstract": "The Hadley Centre Hadley Centre Coupled Model Version 3 was developed from the earlier HadCM2 model in the period 1997-2000. Various improvements were applied to the 19 level atmosphere model and the 20 level ocean model and as a result the model requires no artificial flux adjustments to prevent excessive climate drift. The atmosphere and ocean exchange information once per day, heat and water fluxes being conserved exactly. The main differences from the previous HadCM2 model are a significantly more sophisticated radiation scheme; the inclusion of the direct impact of convection on momentum; and the inclusion of a new land surface scheme that includes a better representation of evaporation, freezing and melting of soil moisture. It improved on the resolution available from previous Hadley Centre models and included support for interactive couplings between the atmosphere and ocean and the biosphere, atmospheric chemistry, the sulphur cycle and atmospheric aerosols. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report." }, "procedureCompositeProcess": null, "imageDetails": [ 157 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2613, "accessConstraints": null, "accessCategory": "restricted", "accessRoles": "link", "label": "restricted: link group", "licence": { "ob_id": 12, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/ukmo_agreement.pdf", "licenceClassifications": [ { "ob_id": 4, "classification": "academic" } ] } } ], "projects": [ { "ob_id": 13847, "uuid": "15b9a832e8964cd89048b0005d3fc9bf", "short_code": "proj", "title": "Met Office Hadley Centre - Modelling", "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": [ 71374, 71375, 71376, 71379, 71381, 71382, 71383, 71384, 71385, 71386, 71388, 71389, 71390, 71392, 71393, 71395, 71396, 71398, 71399, 71400, 71402, 71403, 71404, 71405, 71406, 71407, 71408, 71409, 71410, 71411, 71413, 71414, 71415, 71416, 71417, 71418, 71419, 71420, 71421, 71422, 71424, 71426, 71427, 71428, 71429, 71430, 71431, 71433, 71434, 71435, 71437, 71438, 71439, 71440, 71441, 71442, 71443, 71444, 71445, 71446, 71447, 71448, 71449, 71450, 71451, 71453, 71454, 71455, 71456, 71457, 71458, 71459, 71460, 71461, 71462, 71463, 71464, 71465, 71466, 71468, 71469, 71470, 71471, 71472, 71473, 71474, 71475, 71476, 71478, 71479, 71480, 71481, 71482, 71483, 71484, 71485, 71486, 71487, 71488, 71490, 71493, 72229, 72230, 72231, 72232, 72233, 72234, 72235, 72236, 72237, 72238, 72239, 72240, 72241, 72242, 72243, 72244, 72245, 72246, 72247, 72248, 72249, 72250, 72251, 72252, 72253, 72254, 72255, 72256, 72257, 72258, 72259, 72260, 72261, 72262, 72263, 72264, 72265, 72266, 72267, 72268, 72269, 72270, 72271, 72272, 72273, 72274, 72275, 72276, 72277, 72278, 72279, 72280, 72281, 72282 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 3791, "uuid": "c5f77b9eade060988ee9b067678aaabc", "short_code": "coll", "title": "Met Office Hadley Centre Coupled Model 3 (HadCM3) data", "abstract": "Numerical model data from various Hadley Centre coupled model 3 (HadCM3) experiments. These data cover various time periods, but for the climate change experimenst are typically over the range 1989-2100 and contains all atmospheric fields derived from the HadCM3 model, at various time resolutions." } ], "responsiblepartyinfo_set": [ 199105, 199106, 199107, 199108, 199109, 199110, 199111, 199112, 199113 ], "onlineresource_set": [ 85227, 85228, 85229 ] }, { "ob_id": 40953, "uuid": "75d3d4e546484dd193df5ee172d4520f", "title": "Control run for the HadCM3 model", "abstract": "The Control Run simulation contained in this dataset was used to initialise transient coupled HadCM3 simulations using a variety of emission scenarios. It uses fixed forcing representative of late nineteenth century atmospheric conditions. The control run was initialised and spun up as defined in Johns et al. (2003).\r\n \r\n The Control Run simulation contained in this dataset were used to initialise transient coupled model simulations using HadCM3 for a variety of scenarios. It uses fixed forcing representative of late nineteenth conditions. The control run was initialised and spun up as defined in Johns et al. (2003). The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: aaxzc aaxzk aaxzp aaxzw abxpa abxpb abxpf abxpg abxph abxpi\r\n\r\n Boundary conditions: Fixed forcing representative of late nineteenth century atmospheric conditions.\r\nReferences: \r\nJohns, T.C., J.M. Gregory, W.J. Ingram, C.E. Johnson, A. Jones, J.A. Lowe, J.F.B. Mitchell, D.L. Roberts, D.M.H Sexton, D.S Stevenson, S.F.B. Tett, M.J. Woodage (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics, pp583-612.\r\n\r\n Initial conditions: The model is initialised directly from the Levitus observed ocean state (Levitus and Boyer 1994; Levitus et al. 1995). There is no spinup with surface or interior ocean relaxation; the model runs freely from the start.\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.\r\n\r\nList of climate variables:\r\nSURFACE TEMPERATURE AFTER TIMESTEP\r\nSURFACE ZONAL CURRENT AFTER TIMESTEP\r\nSURFACE MERID CURRENT AFTER TIMESTEP\r\nFRAC OF SEA ICE IN SEA AFTER TSTEP\r\nSEA ICE DEPTH (MEAN OVER ICE) M\r\nGEOPOTENTIAL HEIGHT: PRESSURE LEVELS\r\nRELATIVE HUMIDITY WRT ICE ON P LVS\r\nPRESSURE AT MEAN SEA LEVEL\r\nU COMPNT OF WIND AFTER TIMESTEP\r\nV COMPNT OF WIND AFTER TIMESTEP\r\nTHETA AFTER TIMESTEP\r\nSPECIFIC HUMIDITY AFTER TIMESTEP\r\nCONV CLOUD AMOUNT AFTER TIMESTEP\r\nCONV CLOUD LIQUID WATER PATH\r\nSNOW AMOUNT OVER LAND AFT TSTP KG/M2\r\nSURFACE TEMPERATURE AFTER TIMESTEP\r\nBOUNDARY LAYER DEPTH AFTER TIMESTEP\r\nFRAC OF SEA ICE IN SEA AFTER TSTEP\r\nSEA ICE DEPTH (MEAN OVER ICE) M\r\nNET DOWN SURFACE SW FLUX: SW TS ONLY\r\nNET DN SW RAD FLUX:OPEN SEA:SEA MEAN\r\nNET DOWN SURFACE SW FLUX BELOW 690NM\r\nINCOMING SW RAD FLUX (TOA): ALL TSS\r\nOUTGOING SW RAD FLUX (TOA)\r\nCLEAR-SKY (II) UPWARD SW FLUX (TOA)\r\nCLEAR-SKY (II) DOWN SURFACE SW FLUX\r\nCLEAR-SKY (II) UP SURFACE SW FLUX\r\nSW HEATING RATES: ALL TIMESTEPS\r\nCLEAR-SKY SW HEATING RATES\r\nTOTAL DOWNWARD SURFACE SW FLUX\r\nNET DOWNWARD SW FLUX AT THE TROP.\r\nUPWARD SW FLUX AT THE TROP.\r\nNET DOWN SURFACE LW RAD FLUX\r\nNET DN LW RAD FLUX:OPEN SEA:SEA MEAN\r\nTOTAL CLOUD AMOUNT IN LW RADIATION\r\nOUTGOING LW RAD FLUX (TOA)\r\nCLEAR-SKY (II) UPWARD LW FLUX (TOA)\r\nDOWNWARD LW RAD FLUX: SURFACE\r\nCLEAR-SKY (II) DOWN SURFACE LW FLUX\r\nLW HEATING RATES\r\nCLEAR-SKY LW HEATING RATES\r\nNET DOWNWARD LW FLUX AT THE TROP.\r\nTOTAL DOWNWARD LW FLUX AT THE TROP.\r\nOZONE MASS MIXING RATIO AFTER LW\r\nHT FLUX THROUGH SEAICE:SEA MEAN W/M2\r\nHT FLUX FROM SURF TO DEEP SOIL LEV 1\r\nSURFACE HEAT FLUX W/M2\r\nX-COMP OF SURF & BL WIND STRESS N/M2\r\nY-COMP OF SURF & BL WIND STRESS N/M2\r\nSURFACE TOTAL MOISTURE FLUX KG/M2/S\r\nWIND MIX EN'GY FL TO SEA:SEA MN W/M2\r\n10 METRE WIND U-COMP B GRID\r\n10 METRE WIND V-COMP B GRID\r\nSFC SH FLX FROM OPEN SEA:SEA MN W/M2\r\nEVAP FROM OPEN SEA: SEA MEAN KG/M2/S\r\nSURFACE LATENT HEAT FLUX W/M2\r\nSEAICE TOP MELT LH FLX:SEA MEAN W/M2\r\nTEMPERATURE AT 1.5M\r\nSPECIFIC HUMIDITY AT 1.5M\r\nDEEP SOIL TEMPERATURE AFTER B.LAYER\r\nRELATIVE HUMIDITY AT 1.5M\r\nCANOPY CONDUCTANCE M/S\r\nGROSS PRIMARY PRODUCTIVITY KG C/M2/S\r\nNET PRIMARY PRODUCTIVITY KG C/M2/S\r\nPLANT RESPIRATION KG/M2/S\r\nEVAP FROM SOIL SURF : RATE KG/M2/S\r\nEVAP FROM CANOPY : RATE KG/M2/S\r\nSUBLIM. SURFACE (GBM) : RATE KG/M2/S\r\nLARGE SCALE RAINFALL RATE KG/M2/S\r\nLARGE SCALE SNOWFALL RATE KG/M2/S\r\nCONVECTIVE RAINFALL RATE KG/M2/S\r\nCONVECTIVE SNOWFALL RATE KG/M2/S\r\nCONV. 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It uses fixed forcing representative of late nineteenth century atmospheric conditions. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: aenwp\r\n\r\n Boundary conditions: Fixed forcing representative of late nineteenth century atmospheric conditions as defined in Johns et al. (2003).\r\nReferences: \r\nJohns, T.C., J.M. Gregory, W.J. Ingram, C.E. Johnson, A. Jones, J.A. Lowe, J.F.B. Mitchell, D.L. Roberts, D.M.H Sexton, D.S Stevenson, S.F.B. Tett, M.J. Woodage (2003) Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dynamics, pp583-612.\r\n\r\n Initial conditions: The model is initialised directly from the flux adjusted QUMP project spinup run (run: aenwd date: 2109-12-01)\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T14:45:37.515457", "lastUpdatedDate": "2023-11-08T14:45:37", "latestDataUpdateTime": "2024-09-11T13:10:21", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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This dataset is the third ensemble member for the HadCM3-ALL ensemble.\r\n \r\n The HadCM3-ALL experiment was designed to simulate the combined of all natural and anthropogenic forcings supported by the HadCM3 model. The forcings are based upon historical observations as defined in the IPCC IS95a emission scenario. The HadCM3 model was used by the Hadley Centre to provide input for the IPCC Third Assessment Report.\r\n \r\n Data members run for this experiment: ['acbna', 'acbnc', 'acbne', 'acbni']\r\n\r\n Boundary conditions: The HadCM3-ALL3 simulation includes time varying forcing from major and minor greenhouse gases, anthropogenic sulfur cycle with direct and indirect sulphate aerosol effects, and variations in tropospheric and stratospheric ozone based partly on off-line chemistry calculations broadly consistent with the IPCC IS95a emission scenario from 1859 to 2002 as well as changes in total solar irradiance from Lean, et. al. (1995) and changes in volcanic aerosol from Sato et al. (1993).\r\nReferences: \r\nLean, J., J. Beer, and R. Bradley (1995), Reconstruction of Solar Irradiance Since 1610: Implications for Climate Change. Geophysical Research Letters, 22, 3195-3198.\r\nSato, M., J.E. Hansen, M.P. McCormick, and J.B. Pollack (1995), Stratospheric aerosol optical depths, 1850-1990. Journal of Geophysical Research, 98, 22987-22994.\r\n\r\n\r\n Initial conditions: The third element of the HadCM3-ALL ensemble was initialised with the December 2059 conditions from the HadCM3 control run (HadCM3-ctrl).\r\n\r\n More detailed metadata on the model configuration and parameters is available in XML format.", "creationDate": "2023-11-08T15:15:56.179147", "lastUpdatedDate": "2023-11-08T15:15:56", "latestDataUpdateTime": "2024-09-11T13:10:22", "updateFrequency": "notPlanned", "dataLineage": "Data from the UK Met Office Hadley Centre. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Jet Propulsion Laboratory, Pasadena, California, USA.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-01-18T03:17:20", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. Please see the TCCON website for more information: https://tccondata.org/" }, "imageDetails": [ 224 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2562, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 33, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/TCCON_data_license.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40019, "uuid": "3bfb7dfe4d354fb99864ae1d3de092c6", "short_code": "proj", "title": "Total Carbon Column Observing Network (TCCON)", "abstract": "The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier Transform Spectrometers that record atmospheric transmission spectra in the near-infrared. 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This mirror is part of CEDA to facilitate JASMIN integration of the dataset.\r\n\r\nThe current Data Use Policy is available at https://tccon-wiki.caltech.edu/Main/DataUsePolicy\r\nSee project page for more details." } ], "responsiblepartyinfo_set": [ 199416, 199417, 199418, 199419, 199420, 199421, 199422, 199424, 199425, 199423, 199426, 199427, 199428, 199429, 199430, 199431 ], "onlineresource_set": [ 85356, 85357 ] }, { "ob_id": 40988, "uuid": "b8cd7c1a3b7d4fd3814033fea73a7655", "title": "Total Carbon Column Observing Network (TCCON): TCCON data from Jet Propulsion Laboratory (US), 2011, Release GGG2020.R0", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Jet Propulsion Laboratory, Pasadena, California, USA.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-01-18T03:17:26", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. Please see the TCCON website for more information: https://tccondata.org/" }, "imageDetails": [ 224 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2562, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 33, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/TCCON_data_license.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40019, "uuid": "3bfb7dfe4d354fb99864ae1d3de092c6", "short_code": "proj", "title": "Total Carbon Column Observing Network (TCCON)", "abstract": "The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier Transform Spectrometers that record atmospheric transmission spectra in the near-infrared. The data held here in the CEDA archive are a mirror of the canonical repository for TCCON level 2 data located at https://tccondata.org/.\r\n\r\nThe observations are made using the solar occultation technique, where the sun provides the background radiation against which atmospheric transmission is recorded. From these spectra, accurate and precise column-averaged abundances of primary greenhouse gases are retrieved. Data products include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO). 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This mirror is part of CEDA to facilitate JASMIN integration of the dataset.\r\n\r\nThe current Data Use Policy is available at https://tccon-wiki.caltech.edu/Main/DataUsePolicy\r\nSee project page for more details." } ], "responsiblepartyinfo_set": [ 199432, 199433, 199434, 199435, 199436, 199437, 199438, 199440, 199441, 199439, 199442, 199443, 199444 ], "onlineresource_set": [ 85353, 85354 ] }, { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later.", "creationDate": "2022-11-16T09:59:58.958249", "lastUpdatedDate": "2022-11-16T10:41:02", "latestDataUpdateTime": "2024-03-09T03:21:23", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by scientists on the DCMEX field campaign, with metadata added in accordance with the NCAS-IMAGE-1.0 metadata standard before archiving.", "removedDataReason": "", "keywords": "DCMEX,Camera,Images, AMOF, stereoscopic", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "final", "dataPublishedTime": "2023-12-15T14:19:37", "doiPublishedTime": "2023-12-15T14:22:55.627406", "removedDataTime": null, "geographicExtent": { "ob_id": 3686, "bboxName": "", "eastBoundLongitude": -106.89791, "westBoundLongitude": -106.89798, "southBoundLatitude": 34.022435, "northBoundLatitude": 34.022745 }, "verticalExtent": null, "result_field": { "ob_id": 42895, "dataPath": "/badc/ncas-mobile/data/ncas-cam-12/20220621_dcmex/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 54574269884, "numberOfFiles": 10236, "fileFormat": "Data are JPG formatted." }, "timePeriod": { "ob_id": 10873, "startTime": "2022-07-15T00:00:00", "endTime": "2022-08-04T00:00:00" }, "resultQuality": { "ob_id": 4128, "explanation": "", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2022-11-16" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41069, "uuid": "228be7aa7c1a484284dbf8607dcb3430", "short_code": "acq", "title": "DCMEX NCAS cam 12 deployment", "abstract": "DCMEX NCAS cam 12 deployment" }, "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": [], "vocabularyKeywords": [], "identifier_set": [ 12790 ], "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": [ 199452, 199453, 199454, 199455, 199456, 199457, 199458, 200350, 199459, 199460, 199461, 199462, 199463, 199464, 199465, 199466, 199467, 199468 ], "onlineresource_set": [ 85263, 85264 ] }, { "ob_id": 40993, "uuid": "94333c1e25e046699190745e61e833db", "title": "Total Carbon Column Observing Network (TCCON): TCCON data from Karlsruhe (DE), Release GGG2020.R1", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Karlsruhe, Germany.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-01-18T03:17:19", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. The Caltech page for this dataset can be found here: https://doi.org/10.14291/tccon.ggg2020.karlsruhe01.R1", "removedDataReason": "", "keywords": "TCCON, Greenhouse gases, carbon dioxide, methane, carbon monoxide, nitrous oxide, ground-based, atmospheric trace gases, column-averaged dry-air mole fractions, remote sensing, FTIR spectroscopy", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-12-08T09:52:33", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4035, "bboxName": "Karlsruhe, Germany", "eastBoundLongitude": 8.403653, "westBoundLongitude": 8.403653, "southBoundLatitude": 49.006889, "northBoundLatitude": 49.006889 }, "verticalExtent": null, "result_field": { "ob_id": 41041, "dataPath": "/neodc/tccon/tccon_mirror/data/karlsruhe01/ggg2020", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 46128580, "numberOfFiles": 4, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 11397, "startTime": "2014-01-15T00:00:00", "endTime": "2023-06-26T23:59:59" }, "resultQuality": { "ob_id": 4466, "explanation": "Data as provided by the Caltech TCCON network", "passesTest": true, "resultTitle": "CEDA TCCON Data Quality Statement", "date": "2023-12-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41200, "uuid": "4982d2d7984843048f55a207eafe4f26", "short_code": "cmppr", "title": "Composite Process for: TCCON Caltech network", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Tsukuba, Ibaraki, Japan, 125HR.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-01-18T03:17:37", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. Please see the TCCON website for more information: https://tccondata.org/" }, "imageDetails": [ 224 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2562, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 33, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/TCCON_data_license.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40019, "uuid": "3bfb7dfe4d354fb99864ae1d3de092c6", "short_code": "proj", "title": "Total Carbon Column Observing Network (TCCON)", "abstract": "The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier Transform Spectrometers that record atmospheric transmission spectra in the near-infrared. The data held here in the CEDA archive are a mirror of the canonical repository for TCCON level 2 data located at https://tccondata.org/.\r\n\r\nThe observations are made using the solar occultation technique, where the sun provides the background radiation against which atmospheric transmission is recorded. From these spectra, accurate and precise column-averaged abundances of primary greenhouse gases are retrieved. Data products include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO). More information is available from the Wikipedia page and the Caltech wiki page: https://en.wikipedia.org/wiki/Total_Carbon_Column_Observing_Network and https://tccon-wiki.caltech.edu/Main/TCCON" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 63576, 73968, 73969, 73970, 73971, 73972, 73973, 73974, 73975, 73976, 73977, 73978, 73979, 73980, 73981, 73982, 73983, 73984, 73985, 73986, 73987, 73988, 73989, 73990, 73991, 73992, 73993, 73994, 73995, 73996, 73997, 73998, 73999, 74000, 74001, 74002, 74003, 74004, 74005, 74006, 74007, 74008, 74009, 74010, 74011, 74012, 74013, 74014, 74015, 74016, 74017, 74018, 74019, 74020, 74021, 74022, 74023, 74024, 74025, 74026, 74027, 74028, 74029, 74030, 74031, 74032, 74033, 74034, 74035, 74036, 74037, 74038, 74039, 74040, 74041, 74042, 74043, 74044, 74045, 74046, 74047, 74048, 74049, 74050, 74051, 74052, 74053, 74054 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41059, "uuid": "39f15efce1d748c584c4056ed0eb9669", "short_code": "coll", "title": "Total Carbon Column Observing Network (TCCON): All network sites", "abstract": "This repository is a mirror from the Caltech TCCON data repository located at https://tccondata.org/. This mirror is part of CEDA to facilitate JASMIN integration of the dataset.\r\n\r\nThe current Data Use Policy is available at https://tccon-wiki.caltech.edu/Main/DataUsePolicy\r\nSee project page for more details." } ], "responsiblepartyinfo_set": [ 199688, 199689, 199690, 199691, 199692, 199694, 199686, 199687, 199695, 199693, 199696, 199697 ], "onlineresource_set": [ 85320, 85321 ] }, { "ob_id": 41011, "uuid": "0e591cb194c04b63a60c18518ee9193d", "title": "Total Carbon Column Observing Network (TCCON):TCCON data from Wollongong (AU), Release GGG2020.R0", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Wollongong, Australia.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-01-18T03:17:36", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. 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From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. Please see the TCCON website for more information: https://tccondata.org/" }, "imageDetails": [ 224 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2562, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 33, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/TCCON_data_license.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40019, "uuid": "3bfb7dfe4d354fb99864ae1d3de092c6", "short_code": "proj", "title": "Total Carbon Column Observing Network (TCCON)", "abstract": "The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier Transform Spectrometers that record atmospheric transmission spectra in the near-infrared. The data held here in the CEDA archive are a mirror of the canonical repository for TCCON level 2 data located at https://tccondata.org/.\r\n\r\nThe observations are made using the solar occultation technique, where the sun provides the background radiation against which atmospheric transmission is recorded. From these spectra, accurate and precise column-averaged abundances of primary greenhouse gases are retrieved. Data products include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO). More information is available from the Wikipedia page and the Caltech wiki page: https://en.wikipedia.org/wiki/Total_Carbon_Column_Observing_Network and https://tccon-wiki.caltech.edu/Main/TCCON" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 63576, 73968, 73969, 73970, 73971, 73972, 73973, 73974, 73975, 73976, 73977, 73978, 73979, 73980, 73981, 73982, 73983, 73984, 73985, 73986, 73987, 73988, 73989, 73990, 73991, 73992, 73993, 73994, 73995, 73996, 73997, 73998, 73999, 74000, 74001, 74002, 74003, 74004, 74005, 74006, 74007, 74008, 74009, 74010, 74011, 74012, 74013, 74014, 74015, 74016, 74017, 74018, 74019, 74020, 74021, 74022, 74023, 74024, 74025, 74026, 74027, 74028, 74029, 74030, 74031, 74032, 74033, 74034, 74035, 74036, 74037, 74038, 74039, 74040, 74041, 74042, 74043, 74044, 74045, 74046, 74047, 74048, 74049, 74050, 74051, 74052, 74053, 74054 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41059, "uuid": "39f15efce1d748c584c4056ed0eb9669", "short_code": "coll", "title": "Total Carbon Column Observing Network (TCCON): All network sites", "abstract": "This repository is a mirror from the Caltech TCCON data repository located at https://tccondata.org/. This mirror is part of CEDA to facilitate JASMIN integration of the dataset.\r\n\r\nThe current Data Use Policy is available at https://tccon-wiki.caltech.edu/Main/DataUsePolicy\r\nSee project page for more details." } ], "responsiblepartyinfo_set": [ 199698, 199699, 199700, 199701, 199702, 199703, 199704, 199706, 199705, 199707, 199708, 199709, 199710, 199711, 199712, 199713, 199714, 199715, 199716, 199717, 199718, 199719 ], "onlineresource_set": [ 85323, 85324 ] }, { "ob_id": 41012, "uuid": "d775aa73371c4f1f8f59aded37f1a15c", "title": "Total Carbon Column Observing Network (TCCON): TCCON data from Xianghe, China, Release GGG2020.R0", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Xianghe, China.", "creationDate": "2023-05-11T13:12:53.488270", "lastUpdatedDate": "2023-05-11T13:12:00", "latestDataUpdateTime": "2025-02-19T14:56:54", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).This dataset is part of a mirror archive of the Caltech TCCON network. The Caltech page for this dataset can be found here: https://doi.org/10.14291/tccon.ggg2020.xianghe01.R0", "removedDataReason": "", "keywords": "TCCON, Greenhouse gases, carbon dioxide, methane, carbon monoxide, nitrous oxide, ground-based, atmospheric trace gases, column-averaged dry-air mole fractions, remote sensing, FTIR spectroscopy", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-12-08T09:47:11", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4051, "bboxName": "Xianghe, China", "eastBoundLongitude": 116.9833294, "westBoundLongitude": 116.9833294, "southBoundLatitude": 39.749997, "northBoundLatitude": 39.749997 }, "verticalExtent": null, "result_field": { "ob_id": 41058, "dataPath": "/neodc/tccon/tccon_mirror/data/xianghe01/ggg2020", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 177400834, "numberOfFiles": 6, "fileFormat": "netcdf" }, "timePeriod": { "ob_id": 11415, "startTime": "2018-06-14T00:00:00", "endTime": "2023-05-29T23:59:59" }, "resultQuality": { "ob_id": 4466, "explanation": "Data as provided by the Caltech TCCON network", "passesTest": true, "resultTitle": "CEDA TCCON Data Quality Statement", "date": "2023-12-04" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 41200, "uuid": "4982d2d7984843048f55a207eafe4f26", "short_code": "cmppr", "title": "Composite Process for: TCCON Caltech network", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including CO2, CH4, N2O, HF, CO, H2O, and HDO, are retrieved. Please see the TCCON website for more information: https://tccondata.org/" }, "imageDetails": [ 224 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2562, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 33, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/TCCON_data_license.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 40019, "uuid": "3bfb7dfe4d354fb99864ae1d3de092c6", "short_code": "proj", "title": "Total Carbon Column Observing Network (TCCON)", "abstract": "The Total Carbon Column Observing Network (TCCON) is a global network of ground-based Fourier Transform Spectrometers that record atmospheric transmission spectra in the near-infrared. The data held here in the CEDA archive are a mirror of the canonical repository for TCCON level 2 data located at https://tccondata.org/.\r\n\r\nThe observations are made using the solar occultation technique, where the sun provides the background radiation against which atmospheric transmission is recorded. From these spectra, accurate and precise column-averaged abundances of primary greenhouse gases are retrieved. Data products include carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO). More information is available from the Wikipedia page and the Caltech wiki page: https://en.wikipedia.org/wiki/Total_Carbon_Column_Observing_Network and https://tccon-wiki.caltech.edu/Main/TCCON" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 63576, 73968, 73969, 73970, 73971, 73972, 73973, 73974, 73975, 73976, 73977, 73978, 73979, 73980, 73981, 73982, 73983, 73984, 73985, 73986, 73987, 73988, 73989, 73990, 73991, 73992, 73993, 73994, 73995, 73996, 73997, 73998, 73999, 74000, 74001, 74002, 74003, 74004, 74005, 74006, 74007, 74008, 74009, 74010, 74011, 74012, 74013, 74014, 74015, 74016, 74017, 74018, 74019, 74020, 74021, 74022, 74023, 74024, 74025, 74026, 74027, 74028, 74029, 74030, 74031, 74032, 74033, 74034, 74035, 74036, 74037, 74038, 74039, 74040, 74041, 74042, 74043, 74044, 74045, 74046, 74047, 74048, 74049, 74050, 74051, 74052, 74053, 74054 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41059, "uuid": "39f15efce1d748c584c4056ed0eb9669", "short_code": "coll", "title": "Total Carbon Column Observing Network (TCCON): All network sites", "abstract": "This repository is a mirror from the Caltech TCCON data repository located at https://tccondata.org/. This mirror is part of CEDA to facilitate JASMIN integration of the dataset.\r\n\r\nThe current Data Use Policy is available at https://tccon-wiki.caltech.edu/Main/DataUsePolicy\r\nSee project page for more details." } ], "responsiblepartyinfo_set": [ 199722, 199723, 199724, 199725, 199726, 199720, 199721, 199728, 199729, 199727, 199730, 199731, 199732 ], "onlineresource_set": [ 85327, 85326 ] }, { "ob_id": 41072, "uuid": "05d65cb906084078bc913d1c8d688f69", "title": "Greater Haig Fras autonomous underwater vehicle seafloor survey - mosaicked image tiles used to assess benthic assemblages and seabed types (2012).", "abstract": "Seafloor visual images were acquired during a survey within the Greater Haig Fras Marine Conservation Zone (MCZ), central Celtic Sea, in 2012. This was the first in a series of similar surveys to be conducted in this location. A camera system mounted on the autonomous underwater vehicle (AUV) Autosub6000 was deployed during RRS Discovery cruise 377/8 (D377/8), and images were collected from four 4.7 km transect lines. Images were mosaicked in \"tiles\" consisting of five consecutive images (each tile representing approximately 7.3 m2 of seabed). Images were orthorectified and scaled to a common altitude per tile. The mosaicked tiles are provided in this collection. The aim of the survey was to undertake high-resolution acoustic seabed mapping and visual imagery in a Marine Protected Area, in order to highlight the capability of AUV technology for offshore seabed mapping and benthic assemblage assessment. The work was initially undertaken as part of a Defra-funded project \"Investigating the feasibility of utilizing AUV and Glider technology for mapping and monitoring of the UK MPA network (MB0118)\", Case study 2: Shallow-water AUV mapping off SW UK (https://nora.nerc.ac.uk/id/eprint/500733/) and the Natural Environment Research Council (NERC) funded Autonomous Ecological Surveying of the Abyss project (NE/H021787/1), involving scientists from the National Oceanography Centre (NOC), UK.", "creationDate": "2023-11-27T17:26:13.080627", "lastUpdatedDate": "2023-11-27T17:26:13", "latestDataUpdateTime": "2023-11-27T17:26:13", "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": "final", "dataPublishedTime": "2023-11-27T16:00:00", "doiPublishedTime": "2023-11-27T16:31:56", "removedDataTime": null, "geographicExtent": { "ob_id": 4052, "bboxName": "", "eastBoundLongitude": -7.712078, "westBoundLongitude": -7.720783, "southBoundLatitude": 50.354538, "northBoundLatitude": 50.395287 }, "verticalExtent": null, "result_field": { "ob_id": 41071, "dataPath": "/bodc/USO230175/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 0, "numberOfFiles": 0, "fileFormat": null }, "timePeriod": { "ob_id": 11417, "startTime": "2012-07-26T00:00:00", "endTime": "2012-07-26T23:59:59" }, "resultQuality": { "ob_id": 4462, "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": "2023-11-27" }, "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": [ 12744 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 199782, 199781, 199780, 199779, 199785, 199786, 199787, 199784, 204757, 199789, 199788, 199790, 204758, 199791 ], "onlineresource_set": [] }, { "ob_id": 41073, "uuid": "5f1413b1adf8441f8e785d82f267e58d", "title": "Shadowgraph visualisations of density-stratified turbulence obtained in an inclined duct experiment", "abstract": "Laboratory experiments are used to create salt-stratified turbulence via exchange flow through a long tilted duct (L=2000, H=50, W=100 mm) connecting two large reservoirs (400 litres each) containing saltwater at different densities. This setup allows the study of various turbulent mixing processes responsible for transporting mass and momentum in stratified fluid systems like the oceans. \r\nThe data consist of shadowgraph visualisations, where parallel light is shined through the flow and projected on a semi transparent screen, where it is recorded by a video camera. Movies shows the evolution of density (salinity) contrasts in the flow, including the formation and break up of density interfaces, as well as scouring and overturning behaviours. \r\nA total of 113 movies are made available, each corresponding to a distinct experiment, and collectively covering a two-dimensional parameter space (Reynolds number and tilt angle) where a variety of turbulent behaviours are found.", "creationDate": "2023-11-28T15:33:36.760076", "lastUpdatedDate": "2023-11-28T16:55:10", "latestDataUpdateTime": "2023-11-28T15:33:36", "updateFrequency": "notPlanned", "dataLineage": "Experiments were carried out between January and March 2022 in the G. K. Batchelor Laboratory, selected and post-processed later in 2022 to form the current dataset, which formed the basis of a publication. 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This dataset contains all ensemble members produced by the CCCma CanESM5 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- CanESM5 model reference publications: Swart et al. (2019); Sospedra-Alfonso et al. 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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": [ 6021, 6022, 6023, 9045, 27828, 27829, 50415, 50417, 50418, 50419, 50426, 50427, 50429, 50431, 50445, 50475, 50481, 50496, 50498, 50542, 50543, 50549, 50566, 50577, 50579, 50582, 50587, 50588, 50590, 50597, 50605, 50609, 50613, 50615, 51210, 51211, 52755, 53111, 54228, 54350, 54366, 54378, 59480, 59481, 60438, 62560, 62561, 64080, 66076, 66082, 71613, 74366, 75565, 75566, 75567, 75568 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 41083, "uuid": "01dac4c57559407fb40292389c386d30", "short_code": "coll", "title": "Stratospheric Nudging And Predictable Surface Impacts (SNAPSI): data produced by the CanESM5 model at CCCma", "abstract": "The CanESM5 model contribution to the Stratospheric Nudging And Predictable Surface Impacts (SNAPSI) project produced by scientists at the Canadian Centre for Climate Modelling and Analysis (CCCma). \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- CanESM5 model reference publications: Swart et al. 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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- CanESM5 model reference publications: Swart et al. (2019); Sospedra-Alfonso et al. 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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- CanESM5 model reference publications: Swart et al. (2019); Sospedra-Alfonso et al. 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(2019); Sospedra-Alfonso et al. (2021)" } ], "responsiblepartyinfo_set": [ 199831, 199832, 199833, 199826, 199827, 199828, 199829, 199830, 208156, 208157 ], "onlineresource_set": [ 85419, 85422, 85420, 85421 ] }, { "ob_id": 41085, "uuid": "6db2c027914d4f23bd42409e7aaad3b1", "title": "ICECAPS-ACE: snow-air transition temperatures taken at Summit Station Greenland", "abstract": "This dataset contains point measurement of snow-air transition temperatures at 2 cm intervals on a 5 m thermistor chain installed spanning the snow-air transition at Summit Station, Greenland. Measurements were made using a Snow Ice Mass Balance Apparatus (SIMBA) with a bespoke 5 m chain.\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.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:22:00", "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": "ongoing", "dataPublishedTime": "2023-12-18T14:44:55", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2625, "bboxName": "Summit station greenland", "eastBoundLongitude": -38.46, "westBoundLongitude": -38.46, "southBoundLatitude": 72.575, "northBoundLatitude": 72.575 }, "verticalExtent": null, "result_field": { "ob_id": 41084, "dataPath": "/badc/icecaps-ace/data/snow-temperature-profile", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 146829732, "numberOfFiles": 42, "fileFormat": "Data are NetCDF formatted" }, "timePeriod": { "ob_id": 11440, "startTime": "2022-08-01T00:00:00", "endTime": null }, "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": 41086, "uuid": "15e0f31081c548379d94445e98f2b7d6", "short_code": "acq", "title": "ICECAPS-ACE: Summit Aerosol Cloud Experiment: snow-air temperature taken at Summit Station Greenland", "abstract": "ICECAPS-ACE: Summit Aerosol Cloud Experiment: snow-air temperature taken at Summit Station Greenland" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 236 ], "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": 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, 74108, 74109, 74110, 74111, 74112, 74113, 74114, 74115 ], "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": [ 199843, 199844, 199845, 199846, 199847, 199848, 199849, 199850, 199852, 199851, 199853 ], "onlineresource_set": [ 85430, 85431, 85432, 85433, 85434 ] }, { "ob_id": 41089, "uuid": "063f98575b3b414bb6104053f34f51f0", "title": "FAAM C350 WESCON flight: Airborne atmospheric measurements from core and non-core instrument suites on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core and non-core instrument suites data on board the FAAM BAE-146 aircraft collected for WESCON FAAM Aircraft Project project.", "creationDate": "2023-11-30T11:26:42.685425", "lastUpdatedDate": "2023-11-30T11:26:42.685435", "latestDataUpdateTime": "2025-03-21T16:13:41", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by instrument scientists during the flight before preparation and delivery for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "WESCON, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2023-11-20T10:10:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 4308, "bboxName": "", "eastBoundLongitude": -0.44499683, "westBoundLongitude": -3.1458454, "southBoundLatitude": 50.462402, "northBoundLatitude": 52.369896 }, "verticalExtent": null, "result_field": { "ob_id": 41088, "dataPath": "/badc/faam/data/2023/c350-aug-03", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2180024284, "numberOfFiles": 27, "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted." }, "timePeriod": { "ob_id": 11423, "startTime": "2023-08-03T00:00:00", "endTime": "2023-08-03T23:59:59" }, "resultQuality": { "ob_id": 3074, "explanation": "Data collected by flight participants before preparation for archival with the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "FAAM to CEDA Data Quality Statement", "date": "2015-09-03" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 41090, "uuid": "b839b3f9180f400ab4d9383d9affb021", "short_code": "acq", "title": "FAAM Flight C350 Acquisition", "abstract": "FAAM Flight C350 Acquisition" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 8 ], "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": 37501, "uuid": "3c72b5d38e9a499ead03e9e5f140972d", "short_code": "proj", "title": "WesCon: Wessex Convection experiment", "abstract": "WesCon was a UK summer convection experiment in 2023 funded by the Met Office. It focused on understanding dynamical aspects of convection to provide observational data to develop next generation km scale and urban-scale models to improve prediction of convective storms in km scale Numerical Weather Prediction (NWP) and climate models.\r\n\r\nThe emphasis of WesCon was understanding of dynamical processes (particularly updrafts and turbulence) and their interaction with other processes of importance. \r\n\r\nThe project involved the use of the FAAM Bae-146 aircraft, the Jade-Dimona aircraft and groundbased instruments at Cardington, Netheravon, and nearby ground sites. This project ran in conjunction with WOEST: WesCon - Observing the Evolving Structures of Turbulence project involving groundbased, sonde and radar observations. Both WesCon and WOEST were part of the wider ParaChute programme.\r\n\r\nThe Wessex Convection experiment (WesCon) took place from during summer 2023" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50851, 50853, 50856, 50857, 50934, 50936, 50937, 50942, 50949, 50950, 50952, 50953, 50965, 50967, 50969, 50970, 50971, 50973, 50978, 50979, 50982, 50983, 50984, 50985, 50986, 50987, 50988, 50989, 50990, 50991, 50992, 50993, 50994, 50995, 50996, 50997, 50998, 50999, 51000, 51001, 51002, 51003, 51004, 51005, 51006, 51007, 51008, 51009, 51010, 51011, 51012, 51013, 51014, 51015, 51016, 51017, 51018, 51019, 51020, 51021, 51022, 51023, 51024, 51025, 51026, 51027, 51028, 51029, 51030, 51031, 51032, 51033, 51034, 51035, 51036, 51037, 51038, 51039, 51040, 51041, 51042, 51044, 51045, 51054, 51055, 51056, 51057, 51058, 51059, 51060, 51061, 51062, 51063, 51064, 51065, 51066, 51067, 51068, 51069, 51070, 51071, 51072, 51073, 51074, 51075, 51076, 51077, 51078, 51079, 51080, 51081, 51082, 51083, 51084, 51087, 51222, 51223, 51225, 51227, 51228, 51229, 51230, 51271, 51272, 51279, 51280, 53949, 53951, 53954, 54967, 54971, 54975, 54976, 59964, 59965, 59966, 59967, 59968, 59969, 59970, 59971, 59972, 59973, 59974, 59975, 59976, 60050, 60051, 60052, 60053, 60054, 60055, 60056, 60057, 60058, 60060, 60061, 60062, 60063, 60064, 60065, 60066, 60067, 60068, 60069, 60070, 60071, 60072, 60073, 60074, 60075, 60077, 60078, 60079, 60080, 60081, 60087, 60089, 60090, 60091, 60092, 60093, 60094, 60095, 60096, 60097, 60098, 60099, 60100, 60101, 60102, 60103, 60104, 60105, 60106, 60107, 60108, 60109, 60110, 60111, 60112, 60113, 60114, 60115, 60116, 60117, 60118, 60119, 60172, 60173, 60202, 60203, 60204, 60205, 60206, 60207, 60208, 60209, 60210, 60211, 60212, 60213, 60214, 60215, 60217, 60218, 60219, 60220, 60221, 60222, 60223, 60224, 60225, 60226, 60227, 60228, 60229, 60230, 60231, 60232, 60233, 60234, 60235, 60236, 60237, 60238, 60239, 60240, 60241, 60242, 60243, 60244, 60245, 60246, 60247, 60248, 60249, 60250, 60251, 60252, 60253, 60254, 60255, 60256, 60257, 60258, 60259, 60260, 60261, 60262, 60263, 60264, 60265, 60266, 60267, 60268, 60269, 60270, 60271, 60272, 60273, 60274, 60275, 60276, 60277, 60278, 60279, 60280, 60281, 60282, 60283, 60284, 60287, 62646, 62647, 62648, 62649, 62650, 62651, 62652, 62653, 62654, 62655, 62656, 62657, 62658, 62659, 62660, 62661, 62662, 62663, 62664, 62665, 62666, 62669, 62671, 62678, 72012, 73930, 73931, 73932, 73933, 73934, 73935, 73936, 73937, 73938, 73939, 73940, 73941, 73942, 73943, 73944, 73945, 73946, 73947, 73948, 73949, 73950, 73951, 73952, 73953, 73954, 73955, 73956, 73957, 73958, 73959, 73960, 73961, 73962, 73963, 73964, 73965, 73966, 73967, 74155, 74157 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 5782, "uuid": "affe775e8d8890a4556aec5bc4e0b45c", "short_code": "coll", "title": "Facility for Airborne Atmospheric Measurements (FAAM) flights", "abstract": "The FAAM is a large atmospheric research BAE-146 aircraft, run by the NERC (jointly with the UK Met Office until 2019). It has been in operation since March 2004 and is at the scientists' disposal through a scheme of project selection. \r\n\r\nData collected by this aircraft is stored in the FAAM data archive and includes \"core\" data, provided by the FAAM as a support to all flight campaigns, and \"non-core\" data, the nature of which depends on the scientific goal of the campaign.\r\n\r\nFAAM instruments provide four types of data: \r\n\r\n- parameters required for aircraft navigation; \r\n- meteorology; \r\n- cloud physics; \r\n- chemical composition. \r\n\r\nThe data are accompanied by extensive metadata, including flight logs. The FAAM apparatus includes a number of core instruments permanently onboard and operated by FAAM staff members, and a variety of other instruments, grouped into chemistry kit and cloud physics kit, that can be fitted onto the aircraft on demand. \r\nFAAM is also a member of the EUropean Facility for Airborne Research (EUFAR) fleet of research aircraft.\r\n\r\nAs per NERC data policy (see documents), FAAM data are openly available upon registration with the CEDA archive (anyone can register) under the Open Government Licence. 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": [ 199856, 199857, 199858, 199859, 199860, 199861, 199862, 199863, 199864, 199865 ], "onlineresource_set": [ 85436, 85437 ] } ] }