Observation List
Get a list of Observation objects.
GET /api/v3/observations/?format=api&offset=6400
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It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci 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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27562, 27565, 27566, 27567, 27568, 27569, 27570, 27571, 27573, 27574, 27575, 27576 ], "vocabularyKeywords": [], "identifier_set": [ 10681 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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. 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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. 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The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. 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It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). 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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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27562, 27565, 27566, 27567, 27568, 27569, 27570, 27571, 27573, 27574, 27575, 27576, 27577, 79191, 79192, 79193, 79194, 79195, 79196, 79197, 79198, 79199, 79200, 79201, 79202, 79203, 79204, 79205, 79206, 79207, 79208, 79209, 79210 ], "vocabularyKeywords": [], "identifier_set": [ 10688 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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. 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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": [ 129094, 129100, 129101, 129103, 129107, 129102, 129105, 129104, 129864, 193390, 129095, 129259, 129096, 129097, 129098, 129099 ], "onlineresource_set": [ 36638, 36639, 36640, 36641 ] }, { "ob_id": 29904, "uuid": "d667a28f59664ecdba50fe97dd9d63c7", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Wytham Forest fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Wytham Forest in Oxfordshire, UK and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:15:21", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Wytham Forest , albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:29:30", "doiPublishedTime": "2020-01-30T17:06:18.984434", "removedDataTime": null, "geographicExtent": { "ob_id": 2551, "bboxName": "BACI Wytham", "eastBoundLongitude": -1.3399, "westBoundLongitude": -1.3399, "southBoundLatitude": 51.7773, "northBoundLatitude": 51.7773 }, "verticalExtent": null, "result_field": { "ob_id": 29905, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/01_wytham_forest/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 44546626556, "numberOfFiles": 16, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). 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As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129116, 129115, 129108, 129119, 129120, 129121, 129118, 129114, 129859, 193387, 129109, 129258, 129110, 129111, 129112, 129113 ], "onlineresource_set": [ 36642, 36643, 36644, 36645 ] }, { "ob_id": 29906, "uuid": "26d87003a3684fb69713a4f1bb085861", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Slovenia fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for Slovenia and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-05-22T17:11:21", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Slovenia, albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:34:48", "doiPublishedTime": "2020-01-30T17:11:57.137285", "removedDataTime": null, "geographicExtent": { "ob_id": 2552, "bboxName": "BACI Slovenia", "eastBoundLongitude": 16.0, "westBoundLongitude": 13.0, "southBoundLatitude": 45.4, "northBoundLatitude": 46.5 }, "verticalExtent": null, "result_field": { "ob_id": 29907, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/00_slovenia/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3473184739, "numberOfFiles": 5, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). 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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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27565, 27566, 27573, 27575 ], "vocabularyKeywords": [], "identifier_set": [ 10689 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129128, 129122, 129130, 129131, 129133, 129129, 129135, 129132, 129134, 129865, 193398, 129123, 129257, 129124, 129125, 129126, 129127 ], "onlineresource_set": [ 36646, 36647, 36648, 36649 ] }, { "ob_id": 29908, "uuid": "335ea0c5ed984f2ea5887ecf5d3e0993", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Viterbo fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for Viterbo and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. 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It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci translates satellite data streams into novel “essential biodiversity variables” by integrating ground-based observations. 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WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 18405, 18408, 27561, 27562, 27563, 27564, 27565, 27567, 27568, 27569, 27570, 27571, 27572, 27574, 27575, 27576, 27577, 27578, 79191, 79192, 79193, 79194, 79195, 79196, 79197, 79198, 79199, 79200, 79201, 79202, 79203, 79204, 79205, 79206, 79207, 79208, 79209, 79210 ], "vocabularyKeywords": [], "identifier_set": [ 10692 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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. 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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. 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It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129150, 129157, 129161, 129162, 129158, 129156, 129160, 129163, 204945, 204946, 129151, 129255, 129152, 129153, 129154, 129155 ], "onlineresource_set": [ 36654, 36655, 36656, 36657 ] }, { "ob_id": 29913, "uuid": "aef2d8a023094015971b1dbfdb035abd", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Kruger National Park fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Kruger National Park track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:11:34", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Kruger National Park, albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:31:28", "doiPublishedTime": "2020-01-30T17:08:09.824970", "removedDataTime": null, "geographicExtent": { "ob_id": 2555, "bboxName": "Baci Kruger national Park", "eastBoundLongitude": 32.0, "westBoundLongitude": 31.0, "southBoundLatitude": -27.0, "northBoundLatitude": -22.0 }, "verticalExtent": null, "result_field": { "ob_id": 29914, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/04_kruger/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 9884051497, "numberOfFiles": 16, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci 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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27565, 27566, 27573, 27575 ], "vocabularyKeywords": [], "identifier_set": [ 10684 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129178, 129184, 129186, 129189, 129190, 129185, 129191, 129188, 129861, 193392, 129254, 129179, 129180, 129181, 129182, 129183 ], "onlineresource_set": [ 36665, 36662, 36663, 36664 ] }, { "ob_id": 29915, "uuid": "4d3684dd0d1a4c87b7bec95ff33349e2", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Central Somalia fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Central Somalia fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-05-23T17:29:45", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Central Somalia, albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:32:01", "doiPublishedTime": "2020-01-30T17:08:51.307745", "removedDataTime": null, "geographicExtent": { "ob_id": 2556, "bboxName": "BACI central somalia", "eastBoundLongitude": 50.0, "westBoundLongitude": 43.0, "southBoundLatitude": 3.0, "northBoundLatitude": 8.0 }, "verticalExtent": null, "result_field": { "ob_id": 29916, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/05_c_somalia/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 38958824159, "numberOfFiles": 15, "fileFormat": "netCDF verson 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci 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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27565, 27566, 27573, 27575, 27577, 79191, 79192, 79193, 79194, 79195, 79196, 79197, 79198, 79199, 79200, 79201, 79202, 79203, 79204, 79205, 79206, 79207, 79208, 79209, 79210 ], "vocabularyKeywords": [], "identifier_set": [ 10685 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129192, 129198, 129202, 129201, 129205, 129200, 129199, 129203, 129862, 193389, 129193, 129253, 129194, 129195, 129196, 129197 ], "onlineresource_set": [ 36669, 36666, 36667, 36668 ] }, { "ob_id": 29917, "uuid": "0075727fcd2844a38fc1f03907e0ec5d", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Northern Somalia fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Northern Somalia fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:13:09", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Central Somalia, albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:36:12", "doiPublishedTime": "2020-01-30T17:14:17.128978", "removedDataTime": null, "geographicExtent": { "ob_id": 2559, "bboxName": "BACI northern somalia", "eastBoundLongitude": 50.0, "westBoundLongitude": 43.0, "southBoundLatitude": 6.0, "northBoundLatitude": 10.0 }, "verticalExtent": null, "result_field": { "ob_id": 29918, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/05_n_somalia/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1948256850, "numberOfFiles": 4, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27561, 27562, 27563, 27564, 27566, 27567, 27570, 27572, 27573 ], "vocabularyKeywords": [], "identifier_set": [ 10691 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129212, 129214, 129217, 129218, 129213, 129219, 129216, 129206, 129867, 193388, 129252, 129207, 129208, 129209, 129210, 129211 ], "onlineresource_set": [ 36670, 36672, 36671, 36673 ] }, { "ob_id": 29919, "uuid": "41b281acc6f2404a88232e5c0ef12009", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Southern Somalia fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Southern Somalia fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-05-23T17:50:57", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to CEDA for publication", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Southern Somalia, albedo, mircrowave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:33:11", "doiPublishedTime": "2020-01-30T17:09:42.008679", "removedDataTime": null, "geographicExtent": { "ob_id": 2560, "bboxName": "BACI Southern Somalia", "eastBoundLongitude": 48.0, "westBoundLongitude": 40.0, "southBoundLatitude": -2.0, "northBoundLatitude": 4.0 }, "verticalExtent": null, "result_field": { "ob_id": 29920, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/06_s_somalia/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14195757219, "numberOfFiles": 24, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci 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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27561, 27562, 27563, 27564, 27565, 27566, 27567, 27568, 27569, 27570, 27571, 27572, 27573, 27574, 27575, 27576, 27577, 79191, 79192, 79193, 79194, 79195, 79196, 79197, 79198, 79199, 79200, 79201, 79202, 79203, 79204, 79205, 79206, 79207, 79208, 79209, 79210 ], "vocabularyKeywords": [], "identifier_set": [ 10686 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129220, 129228, 129233, 129229, 129226, 129227, 129231, 129230, 129863, 193397, 129251, 129221, 129222, 129223, 129224, 129225 ], "onlineresource_set": [ 36675, 36674, 36677, 36676 ] }, { "ob_id": 29921, "uuid": "d638387d907648b68fa8578907293d9a", "title": "BACI: System State Vector (SSV) land surface time series dataset for the Romanian fast track site, 2000-2015, v1.0", "abstract": "The BACI Surface State Vector (SSV) dataset for the Romanian fast track site and provides a description of the surface state from a combination of satellite observations across wavelength domains i.e. albedo (visible), Land Surface Temperature (LST) (passive/thermal microwave) and backscatter (active microwave). The dataset contains a unique spatially and temporally consistent (as far as the observations allow) series of observations of the land surface, across optical and microwave domains. The innovation of this approach is in providing a SSV in a common space/time framework, containing information from multiple, independent data streams, with associated uncertainty. The methods used can be used to combine data from multiple different satellite sources. The resulting dataset is intended to make the best use of all available observations to detect changes in the land surface state: the combination of data is likely to show changes that would not be apparent from data in a single wavelength region. The inclusion of uncertainty also allows the strength of the resulting changes to be properly quantified.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-05-23T17:59:36", "updateFrequency": "notPlanned", "dataLineage": "Provided by Mathias Disney of the University College London BACI project team to the Centre for Environmental Data Analysis (CEDA) for publication.", "removedDataReason": "", "keywords": "BACI, TOWARDS A BIOSPHERE ATMOSPHERE CHANGE INDEX, State Surface Vector, Romania, albedo, microwave, backscatter", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-11-14T12:33:48", "doiPublishedTime": "2020-01-30T17:10:25.504960", "removedDataTime": null, "geographicExtent": { "ob_id": 2561, "bboxName": "BACI Romania", "eastBoundLongitude": 28.0, "westBoundLongitude": 22.0, "southBoundLatitude": 44.0, "northBoundLatitude": 48.0 }, "verticalExtent": null, "result_field": { "ob_id": 29922, "dataPath": "/neodc/baci_ssv/data/v1.0/fast_track_sites/07_romania/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12316917236, "numberOfFiles": 23, "fileFormat": "netCDF version 4" }, "timePeriod": { "ob_id": 8136, "startTime": "2000-01-01T00:00:00", "endTime": "2015-12-31T00:00:00" }, "resultQuality": { "ob_id": 3319, "explanation": "BACI data validated by Maxim Chernetskiy UCL project team", "passesTest": true, "resultTitle": "BACI Data Quality Statement", "date": "2019-08-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 27704, "uuid": "5a3bc525bb384291881b16c9aff89365", "short_code": "comp", "title": "BACI State Surface Vector Computation (SSV)", "abstract": "The main requirement for BACI SSV dataset was to provide frequent time series of remote sensing information in different domains of electromagnetic spectrum covering largest possible regions. It was important to have data which allows change detection to be as precise as possible without attribution. The dataset combines layers of optical, thermal infrared and microwave data providing comprehensive set of information. \r\n\r\nThe process used MODIS reflectance, MODIS land surface temperature and Sentinel-1 VV/VH backscatter. It also employed linear Kernel BRDF models to normalise reflectance to nadir view. i.e.and an inversion of the Kernel models to obtain kernels and then it is easy to calculate reflectance at nadir. In the case of thermal and SAR information the process used identity operator i.e. smoother to fill gaps and estimate uncertainty. This allows minimum loss of information and makes data sets compatible.\r\nThe main difference between SSV datasets and conventional way of representing data is availability of information about associated uncertainties. This allows to see the extent to which we can trust specific pixel at specific date/time. Most of the conventional change detection and time series decomposition methods do not take uncertainty into account. This can lead to misinterpretation of data due to atmospheric effects, processing or model errors. The result was smooth continuous time series with associated uncertainties and restored time/space gaps. We exploit temporal regularization which was presented in see {Quaife2010} and {Lewis2012a} in data set documentation). This technique allows filling gaps in the time series of parameters and explicitly characterize the output uncertainties.\r\n\r\nInputs to the BACI SSV are MODIS daily reflectance and LST data, Sentinel 1 backscatter and historical microwave (ENVISAT ASAR). A key innovation of the BACI SSV processing chain is the use of the multitasking facilities of CEMS/JASMIN cluster to process almost 20 years of EO data across domains ." }, "procedureCompositeProcess": null, "imageDetails": [ 214 ], "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": 27702, "uuid": "79d5be6f496e4166b2d2a7d2f1716476", "short_code": "proj", "title": "Towards a Biosphere Atmosphere Change Index (BACI) H2020 project", "abstract": "The “BACI” baci 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. Other key elements are, firstly attributing ecosystem transformations to societal transformations, and secondly developing a prototype early warning system for detecting disturbances at the interface of land ecosystems and atmosphere.\r\n\r\nUniversity College London lead work package 2, provided the core requirement of timely and consistent spatial data to be used as input to the BACI analysis framework. This was primarily Earth Observation Sattelite data, but also additional spatial data such as elevation and slope/aspect. WP2 will provide a generic, scaleable framework for combining data from multiple streams for input into BACI index analysis, effectively a multi-source, surface change detection system. \r\n\r\nThe output of work package 2 was a system ‘state vector’ representing the state of a point/region on the land surface at a given time as a function of input data (reflectance, Δreflectance i.e. change in reflectance since the last observation, LST, backscatter and multi-temporal backscatter statistics, interferometric coherence, soil moisture, freeze/thaw, snow characteristics, albedo, vegetation state, ancillary), with uncertainty archived at CEDA. \r\n\r\nThe project co-ordinated by the Max Planck Institute for Biogeochemistry ran from April 2015 - March 2019\r\n\r\nThis project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 640176" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 27561, 27562, 27563, 27564, 27565, 27566, 27567, 27568, 27569, 27570, 27571, 27572, 27573, 27574, 27575, 27576, 27577, 79191, 79192, 79193, 79194, 79195, 79196, 79197, 79198, 79199, 79200, 79201, 79202, 79203, 79204, 79205, 79206, 79207, 79208, 79209, 79210 ], "vocabularyKeywords": [], "identifier_set": [ 10687 ], "observationcollection_set": [ { "ob_id": 27434, "uuid": "1452fa13390549f5a6794840b948a8d1", "short_code": "coll", "title": "Regularised optical, thermal and backscatter land surface System State Vector (SSV) time series data collection from the BACI ( Towards a Biosphere Atmosphere Change Index) project.", "abstract": "The BACI System State Vector datasets cover large regional sites in Europe, West, Eastern and Southern Africa in addition to smaller fast track sites in Denmark, Wytham Forest, Kruger National Park, Hainich, Viterbo, Romania, Slovenia, Ethiopia and Southern/Central/Northern Somalia. \r\n\r\nThe BACI datasets address one of main complications in combining different Earth Observation (EO) data streams is a requirement of common time and space resolution. These data are gap free time series, of EO data across optical (reflectance, albedo), passive microwave (LST) and active microwave (backscatter) domains. This collection contains optimally smoothed and filtered time series of reflectance, albedo and backscatter datasets, starting in 2000 and running to the present, as the core SSV output. \r\n\r\nCrucially, the SSV data is provided with consistent uncertainties, which is key for use in downstream quantitative modelling and change detection applications, particularly to help attribute and explain detected change. Changes in the Earth’s surface can have very different properties and so can influence very different domains of the electromagnetic spectrum. As a result these datasets are particularly useful for trying to detect changes in ecosystem structure and function, a potentially vital application for satellite monitoring of the Earth system." }, { "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": [ 129241, 129242, 129240, 129234, 129247, 129245, 129244, 129855, 129854, 193399, 129246, 129250, 129235, 129236, 129237, 129238, 129239 ], "onlineresource_set": [ 36678, 36679, 36680, 36681 ] }, { "ob_id": 29929, "uuid": "20babc8f4dc449eaac11f47708e9f721", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 04.5", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created. \r\n\r\nThe v04.5 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2018-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T11:58:29", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the Soil Moisture v04.5 dataset (doi:10.5285/38b8e5e524e1449ab4b4994970752644. http://dx.doi.org/10.5285/38b8e5e524e1449ab4b4994970752644)", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Active", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degrees", "status": "superseded", "dataPublishedTime": "2019-12-19T18:06:17", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29930, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/ACTIVE/v04.5", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 7934506678, "numberOfFiles": 10012, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8138, "startTime": "1991-08-04T23:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3251, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-02-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27120, "uuid": "972ce1b35bbe40d7b8a889d2e65f4fb0", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Active sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The active product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54644, 54645, 54646, 54647, 54648, 54649 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29937, "uuid": "38b8e5e524e1449ab4b4994970752644", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.5 data collection", "abstract": "Soil Moisture data (version 04.5) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31 and the ACTIVE product covering 1991-08-05 to 2018-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 129299, 129295, 129298, 129296, 129297, 129301, 129300, 129302, 129304, 129303, 129305, 129306, 129307, 129308, 129309, 129310, 129311, 129312, 129619, 129313 ], "onlineresource_set": [ 36696, 36690, 36691, 36693, 36694, 36695, 36688, 36687, 36800, 36801, 36802, 36692, 42176 ] }, { "ob_id": 29931, "uuid": "34090d316d514b55b7080a4181a28f8f", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 04.5", "abstract": "The Soil Moisture CCI COMBINED dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. \r\n\r\nThe v04.5 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-11-18T10:01:53", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the Soil Moisture v04.5 dataset (doi:10.5285/38b8e5e524e1449ab4b4994970752644. http://dx.doi.org/10.5285/38b8e5e524e1449ab4b4994970752644)", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, COMBINED", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25", "status": "superseded", "dataPublishedTime": "2019-12-19T18:12:13", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29932, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v04.5", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14622061193, "numberOfFiles": 14672, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8140, "startTime": "1978-11-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3252, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-02-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27141, "uuid": "c92b9dd29dd64d06b18f085bb033f311", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using combined active and passive sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The combined product uses information from both active and passive sensors." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54646, 54647, 54648, 54649, 54650, 54651 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29937, "uuid": "38b8e5e524e1449ab4b4994970752644", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.5 data collection", "abstract": "Soil Moisture data (version 04.5) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31 and the ACTIVE product covering 1991-08-05 to 2018-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 129314, 129315, 129317, 129318, 129319, 129320, 129321, 129316, 129322, 129323, 129324, 129325, 129326, 129327, 129328, 129329, 129330, 129331, 129617, 129332 ], "onlineresource_set": [ 36709, 36703, 36707, 36702, 36705, 36706, 36708, 36704, 36700, 36791, 36790, 36792, 42177 ] }, { "ob_id": 29933, "uuid": "4b344dac208f4a82a35d544f6837d111", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 04.5", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v04.5 PASSIVE product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-11-18T10:01:54", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the Soil Moisture v04.5 dataset (doi:10.5285/38b8e5e524e1449ab4b4994970752644. http://dx.doi.org/10.5285/38b8e5e524e1449ab4b4994970752644)", "removedDataReason": "", "keywords": "ESA, CCI, PASSIVE", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degree", "status": "superseded", "dataPublishedTime": "2019-12-19T18:19:14", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29934, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/PASSIVE/v04.5", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 11957254311, "numberOfFiles": 14672, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8154, "startTime": "1978-11-01T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3148, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27140, "uuid": "6a1bc8226e7749d8bcbff89ca4ae3d93", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Passive sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54646, 54647, 54648, 54649, 54650, 54651 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29937, "uuid": "38b8e5e524e1449ab4b4994970752644", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.5 data collection", "abstract": "Soil Moisture data (version 04.5) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31 and the ACTIVE product covering 1991-08-05 to 2018-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 129333, 129334, 129336, 129337, 129338, 129339, 129340, 129335, 129342, 129341, 129343, 129344, 129345, 129346, 129347, 129348, 129349, 129350, 129620, 129351 ], "onlineresource_set": [ 36715, 36716, 36717, 36714, 36718, 36721, 36723, 36724, 36722, 36797, 36798, 36799, 42178 ] }, { "ob_id": 29935, "uuid": "4a29dbf40fc3445395d72927700fafe7", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 04.5", "abstract": "These ancillary datasets were used in the production of the ACTIVE, PASSIVE and COMBINED soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v04.5 Soil Moisture CCI data.\r\n\r\nThe ACTIVE, PASSIVE and COMBINED soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website.\r\n\r\nSoil moisture CCI data should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:11:19", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the Soil Moisture v04.5 dataset (doi:10.5285/38b8e5e524e1449ab4b4994970752644. http://dx.doi.org/10.5285/38b8e5e524e1449ab4b4994970752644)", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Ancillary", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2019-12-19T18:25:37", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29936, "dataPath": "/neodc/esacci/soil_moisture/data/ancillary/v04.5/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1558808, "numberOfFiles": 6, "fileFormat": "Data are netCDF formatted." }, "timePeriod": null, "resultQuality": { "ob_id": 3151, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 12066, 52664, 52665, 54683, 54684, 54685, 54686, 60753, 60754, 63533, 63534, 63535, 63536 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29937, "uuid": "38b8e5e524e1449ab4b4994970752644", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.5 data collection", "abstract": "Soil Moisture data (version 04.5) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2018-12-31 and the ACTIVE product covering 1991-08-05 to 2018-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 129367, 129369, 129352, 129353, 129354, 129355, 129370, 129368, 129356, 129357, 129358, 129359, 129360, 129361, 129362, 129363, 129364, 129365, 129621, 129366 ], "onlineresource_set": [ 36730, 36732, 36736, 36726, 36733, 36727, 36731, 36735, 36793, 36794, 36795, 36796, 42179 ] }, { "ob_id": 29946, "uuid": "b6b6266415634a6ab242702d7ee6be05", "title": "ESM-SnowMIP meteorological and evaluation datasets at ten reference sites (in situ and bias corrected reanalysis data)", "abstract": "In situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data for cold regions modelling at ten sites: one maritime (Sapporo, Japan), one arctic (Sodankylä, Finland), three boreal (Old Aspen, Old Jack Pine and Old Black Spruce, Saskatchewan, Canada) and five mid-latitude alpine (Col de Porte, France; Reynolds Mountain East, Idaho, USA, Senator Beck and Swamp Angel, Colorado, USA; Weissfluhjoch, Switzerland). \r\n\r\nThe long-term datasets are the reference sites chosen for evaluating models participating in the Earth System Model-Snow Model Intercomparison Project (ESM-SnowMIP). Periods covered by the in situ data span from 1994 to 2016 with the period of available data varying by location from between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow water equivalent, albedo, soil temperature and surface temperature) available at varying temporal intervals. \r\n\r\n30-year (1980-2010) time-series have been extracted from a global gridded surface meteorology dataset (Global Soil Wetness Project Phase 3) for the grid cells containing the reference sites, interpolated to one-hour timesteps and bias corrected.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-05T09:41:53", "updateFrequency": "notPlanned", "dataLineage": "Data are as delivered by ReSCUES project.", "removedDataReason": "", "keywords": "Snow, Long-Term Record, Modelling, Observation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-04-23T16:42:19", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2562, "bboxName": "ESM-SnowMIP", "eastBoundLongitude": 141.3, "westBoundLongitude": -116.8, "southBoundLatitude": 37.9, "northBoundLatitude": 67.4 }, "verticalExtent": null, "result_field": { "ob_id": 29947, "dataPath": "/badc/rescues/data/ESM-SnowMIP", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 330367928, "numberOfFiles": 32, "fileFormat": "Data are netCDF format." }, "timePeriod": { "ob_id": 8188, "startTime": "1980-01-01T00:00:00", "endTime": "2016-12-31T23:59:59" }, "resultQuality": { "ob_id": 3391, "explanation": "The ESM-SnowMIP data were quality controlled by the data providers prior to being made available to CEDA.", "passesTest": true, "resultTitle": "ESM-SnowMIP Data Quality Statement", "date": "2020-03-09" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 29950, "uuid": "907580df58f74572910490e6084d979f", "short_code": "cmppr", "title": "In situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data for the ESM-SnowMIP reference sites.", "abstract": "The Earth System Model - Snow Model Intercomparison Project (ESM-SnowMIP) reference sites are characterised as maritime (Sapporo, Japan), arctic (Sodankylä, Finland), boreal (Old Aspen, Old Jack Pine and Old Black Spruce, Saskatchewan, Canada) and mid-latitude alpine (Col de Porte, France; Reynolds Mountain East, Idaho, USA, Senator Beck and Swamp Angel, Colorado, USA; Weissfluhjoch, Switzerland). The locations of the in situ measurement sites are listed as follows: \r\n\r\nCDP (Col de Porte), Latitude: 45.29, Longitude: 5.77, Elevation: 1325.0 m, Location: France;\r\nOAS (Old Aspen), Latitude: 54.05, Longitude: -106.33, Location: Canada;\r\nOBS (Old Black Spruce), Latitude: 54.65, Longitude: -105.20, Location: Canada;\r\nOJP (Old Jack Pine), Latitude: 54.53, Longitude: -105.00, Location: Canada; \r\nRME (Reynolds Mountain East), Latitude: 43.19, Longitude: -116.78, Location: United States;\r\nSAP (Sapporo), Latitude: 43.06, Longitude: 141.33, Elevation: 17.0 m, Location: Japan;\r\nSNB (Senator Beck Basin Study Area (SBBSA)), Latitude: 37.91, Longitude: -107.73, Location: United States;\r\nSOD (Sodankyla), Latitude: 67.42, Longitude: 26.59, Location: Finland; \r\nSWA (Swamp Angel Study Plot (SASP)), Latitude: 37.91, Longitude: -107.71, Elevation: 3371.0 m, Location: United States; \r\nWFJ (Weissfluhjoch), Latitude: 46.83, Longitude: 9.81, Location: Switzerland." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24878, "uuid": "be196dbbf8ce4aad97ee46e54cd2291e", "short_code": "proj", "title": "Reducing Snow-Climate Uncertainty in Earth System modelling (ReSCUES)", "abstract": "This project uses use high-resolution meteorological data and the same modelling methods that were applied on the hemispheric scale to make and test predictions for snowmelt in well-instrumented areas of the French and Swiss Alps. Methods developed were incorporated in a \"downscaling toolkit\" which will be made available to researchers and water managers by the International Network for Alpine Research Catchment Hydrology.\r\n\r\nSnow is a material with remarkable physical properties that profoundly alters the characteristics of the Earth's surface where it lies. Because snow has a high albedo (the fraction of solar radiation that it reflects rather than absorbs) and a high latent heat of fusion (the energy required to melt it), it delays the warming of the atmosphere and the ground in spring each year. Satellite measurements of Northern Hemisphere snow cover have now been available for 50 years, and a strong decreasing trend correlated with warming has been observed in spring over that period. Less snow accumulates in a warmer climate and melts sooner, increasing the absorption of solar radiation and reinforcing the warming (a strong positive feedback). Snow conducts heat poorly because it contains trapped air and so insulates the ground from cold temperatures in winter; this controls soil freezing and provides protection for short plants, small animals and soil microbes living in snowy regions, with important and complex impacts on the global carbon cycle. For all of these reasons, it is important that climate models should be able to predict snow cover accurately. Unfortunately, the latest climate models still differ greatly in their simulations of how snow cover varies from year to year in the current climate and how it will change in the future. There are many potential sources for this uncertainty, including errors in snowfall and temperature patterns predicted by models, multiple processes that control the rate of snowmelt but may be poorly represented in models, and uncertainty in setting optimal values for model parameters. It has proven very difficult to disentangle these sources of uncertainty and to determine how they can be reduced. In this project, we will use a new modelling system in which a single meteorological variable, model process or parameter value can be varied at a time, allowing highly controlled experiments to precisely determine how they influence simulations. Direct measurements of snow properties at research sites and satellite measurements of snow cover and albedo across the Northern Hemisphere will be used to identify the best simulations. Because snow melts both as the weather warms in spring and as the climate warms, improving the ability of models to simulate the current seasonal cycle and past trends can be expected to improve projections of future conditions, provided that the improvements are obtained for sound physical reasons. Improved predictions and better understanding of the sensitivity of snow to climate change will contribute to reviews of climate science by the Intergovernmental Panel on Climate Change which are essential resources for policymakers. Another important feature of snow is that it stores precipitation that falls in the mountains over winter and releases it in warmer times of year when human demand for water is higher. Many parts of the world are provided with water and threatened by floods from melting snow in upstream mountain regions. Even if the total amount of precipitation does not change in a warming climate, a shift to more falling as rain rather than snow will lead to river flows peaking earlier in the year, requiring major changes in the management of water resources. Global climate models, which cannot resolve processes occurring on scales smaller than a few hundred kilometres, are not adequate tools for informing water management decisions, but national weather services are now beginning to run forecasts for limited areas and short periods with kilometre-scale resolutions. \r\n\r\nNERC Project Reference: NE/P011926/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 61865, 61866, 61867, 61868, 61869, 61870, 61871, 61872, 61873, 61874, 61875, 61876, 61877, 61878, 61879, 61880, 61881, 61882, 61883, 61884, 61885, 61886, 61887, 61888, 61889, 80059, 80060 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130374, 130375, 130377, 130378, 130380, 130382, 130383, 130379, 130376, 130381 ], "onlineresource_set": [ 36755 ] }, { "ob_id": 29951, "uuid": "bedc59f37c9545c981a839eb552e4084", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the year 2017, v1", "abstract": "This dataset comprises estimates of forest above-ground biomass for the year 2017. The data has been produced as part of the European Space Agency's (ESA's) Climate Change Initiative (CCI) programme by the Biomass CCI team. \r\n\r\nThe data products consist of two (2) global layers that include estimates of:\r\n1) above ground biomass (AGB, unit: tons/ha i.e., Mg/ha) for the year 2017 (raster dataset). This is defined as the mass, expressed as oven-dry weight of the woody parts (stem, bark, branches and twigs) of all living trees excluding stump and roots\r\n2) per-pixel estimates of above-ground biomass uncertainty expressed as standard error in Mg/ha (raster dataset)\r\n\r\nThis release of the data is version 1, with data provided in both netcdf and geotiff format.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-10T18:19:57", "updateFrequency": "notPlanned", "dataLineage": "Data was created by the Biomass CCI team. \r\nThe data was derived from input data from the PALSAR-2 instrument on the ALOS-2 satellite, and data from the Sentinel-1 satellite. The ALOS-2 PALSAR-2 dataset was obtained from the Japanese Aerospace Exploration Agency (JAXA) in the form of a mosaic of images acquired in 2017. The Sentinel-1 dataset consisted of individual data takes by the 1A and 1B units during 2017 and provided by the European Space Agency.", "removedDataReason": "", "keywords": "ESA, CCI, Biomass", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2019-12-02T14:00:52", "doiPublishedTime": "2019-12-02T14:05:44", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29960, "dataPath": "/neodc/esacci/biomass/data/agb/maps/2017/v1.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 58203928517, "numberOfFiles": 61, "fileFormat": "Data are provided in both NetCDF and GeoTiff format" }, "timePeriod": { "ob_id": 8152, "startTime": "2017-01-01T00:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 3355, "explanation": "For information on the quality of the data see the documentation and website for the Biomass CCI", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-11-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 29957, "uuid": "292bdc6d6ce24a8497dc05df10d1717a", "short_code": "cmppr", "title": "CCI Biomass", "abstract": "CCI Biomass" }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2542, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 20, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_biomass_terms_and_conditions_v2.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 29954, "uuid": "748552f369494506bc790c8a51854c38", "short_code": "proj", "title": "ESA CCI Biomass Climate Change Initiative (CCI)", "abstract": "The primary science objective ESA’s Climate Change Initiative + Biomass project is to provide global maps of above-ground biomass (Mg ha-1) with these being capable of supporting quantification of biomass change. The mapping aims to achieve 500 m to 1 km spatial resolution with a relative error of less than 20 % where AGB exceeds 50 Mg ha-1. Although this resolution is finer than required for current climate modelling, it will allow more refined information to be inferred (e.g. forest age structure and the disturbance regime) that is relevant for climate and has the potential to be exploited by carbon cycle and climate models as they develop." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 6023, 27612, 27613, 27614, 27615, 27616, 27617 ], "vocabularyKeywords": [ { "ob_id": 11060, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_biomass", "resolvedTerm": "above-ground biomass" }, { "ob_id": 10781, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_alos-2", "resolvedTerm": "ALOS-2" }, { "ob_id": 11139, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_sarcS1", "resolvedTerm": "SAR-C (Sentinel-1)" }, { "ob_id": 10907, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel1a", "resolvedTerm": "Sentinel-1A" }, { "ob_id": 10908, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel1b", "resolvedTerm": "Sentinel-1B" }, { "ob_id": 11147, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_palsar2", "resolvedTerm": "PALSAR-2" } ], "identifier_set": [ 10667 ], "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": [ 129397, 129399, 129400, 129401, 129402, 129404, 129406, 129405, 129403, 129398 ], "onlineresource_set": [ 36768, 36771, 36767, 36762, 89543, 89544, 89525, 89526, 89527, 89528, 89529, 89530, 89531, 89532, 89533, 89534, 89535, 89536, 89537, 89538, 89539, 89540, 89541, 89542, 87509, 87510, 87511, 87512, 87513, 87514, 87515, 87576, 87577, 87578, 95057 ] }, { "ob_id": 29953, "uuid": "5808b237bdb5485d9bc3595f39ce85e3", "title": "SMURPHS: Historical HadGEM3-GC3.1 scaled aerosol coupled ensemble", "abstract": "This dataset consists of 5x5 historical simulations (1850-2014) with HadGEM3-GC3.1 (Met Office Hadley Centre Global Coupled model General Circulation 3.1). This is an ensemble dataset, part of the Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events (SMURPHS) project. The model version used here is a development version of the UK's submission to Coupled Model Intercomparison Project Phase 6 (CMIP6) and differs from the CMIP6 version in its treatment of prescribed ozone. This ensemble was designed to sample a range in plausible historical aerosol forcing, with the present-day aerosol effective radiative forcing ranging from -0.38 W/m2 to -1.5 W/m2, which spans a large range of the total aerosol effective radiative forcing presented in Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report (AR5). \r\n\r\nThe targeted aerosol forcings were achieved by applying a constant scaling factor in space and time to the standard historical CMIP6 historical AA and precursor emissions, namely organic and black carbon (fossil and biofuel) and sulfur dioxide (SO2) emissions. All other forcing agents follow historical CMIP6 emissions. The scalings were chosen such that the targeted aerosol forcings are approximately equally spaced:\r\n- 0.2x scaling to give -0.38 W/m2\r\n- 0.4x scaling to give -0.60 W/m2\r\n- 0.7x scaling to give -0.93 W/m2\r\n- 1.0x scaling to give -1.17 W/m2\r\n- 1.5x scaling to give -1.50 W/m2. \r\n\r\nThe scalings required to reach the intended forcing values were determined from 5 10-year atmosphere-only time-slice runs for the year 2014 with pre-industrial sea-surface temperatures. Note that the 1x scaling is not strictly a ‘scaling’ but corresponds to the standard emissions. The configuration for the scaled aerosol simulations was derived from the 1x scaling simulations, therefore these are directly comparable with the only difference being the scaled aerosol emissions. Five simulations were performed for each scaling and the simulations cover the period 1850-2014. The same five initial conditions were used for each scaling sub-ensemble, and the first four correspond to the initial conditions selected for the four CMIP6 historical simulations. These were well spaced in a pre-industrial control simulation and sample different phases of internal variability in both the Pacific and Atlantic. We recommend only analysing data from 1900, as introducing the scaled aerosols in 1850 produces a small initial drift in the climate system. We estimate that most of this drift has been removed by 1900.\r\n\r\nThe reference paper for this dataset is Dittus et al., 2020. Please cite this paper when using the dataset. Dittus, A. J., Hawkins, E., Wilcox, L. J., Sutton, R. T., Smith, C. J., Andrews, M. B. and P. M. Forster, 2020: Sensitivity of historical climate simulations to uncertain aerosol forcing. In press, Geophysical Research Letters", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2023-07-13T17:06:40", "updateFrequency": "", "dataLineage": "The historical-0p2, historical-0p4, historical-0p7 and historical-1p5 ensemble members (20 in total), as well as the fifth member of historical-1p0, were run by Andrea Dittus. The historical-1p0 ensemble members were run by Martin Andrews. The data were post-processed by Andrea Dittus.\r\n\r\nThe data were passed from the project team to the Centre for Environmental Data Analysis for archival and distribution.", "removedDataReason": "", "keywords": "SMURPHS, coupled, HadGEM3-GC3.1, aerosol, ensemble, historical", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-10-23T08:54:43", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2564, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30279, "dataPath": "/badc/smurphs/data/coupled_ensemble/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1899509956490, "numberOfFiles": 874, "fileFormat": "Data are provided in NetCDF formats." }, "timePeriod": { "ob_id": 8144, "startTime": "1850-01-01T00:00:00", "endTime": "2014-12-30T23:59:59" }, "resultQuality": { "ob_id": 3214, "explanation": "The Centre for Environmental Data Analysis have done no quality control on the data as they are produced by the project team.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-01-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30280, "uuid": "b8d98cd4357d402eb1757ef95e7df1ac", "short_code": "comp", "title": "Development version of Met Office Hadley Centre (MOHC) HadGEM3-GC31-LL", "abstract": "All simulations in the historical scaled aerosol emissions ensemble were conducted with a development version of the HadGEM3-GC3.1-LL global climate model at N96-ORCA1 resolution, corresponding to 135 km resolution in mid-latitudes and 1º in the ocean (Williams et al., 2018). The low-resolution version of this model (used here) is documented in Kuhlbrodt et al. (2018). Information on the representation of aerosol processes and aerosol radiative forcing in this model can be found in Mulcahy et al. (2018). \r\n\r\nThe model is related to this model: http://catalogue.ceda.ac.uk/uuid/014568b39a754538a27e25f24e7f1fca\r\n\r\nThe only difference between the model version used here and the CMIP6 version linked above is in the treatment of prescribed ozone. In the development version (used for this dataset), there is a known issue that causes stratospheric ozone concentrations to occur in the upper troposphere as the tropopause rises with warming, causing a small amount of unphysical warming. This issue has been resolved in the UK's contribution to CMIP6 (Andrews et al., 2020 under review)." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 24884, "uuid": "bf21a2279ff0419db6a637183c748d0c", "short_code": "proj", "title": "Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events (SMURPHS)", "abstract": "The project SMURPHS (Securing Multidisciplinary UndeRstanding and Prediction of Hiatus and Surge events) aims to better understand the causes of periods where the rate of global-mean surface temperature is increased or decreased at decadal time-scales (hereafter referred to as \"surge\" or \"hiatus\" events, or collectively as \"H/S events\") compared with long term (multi-decadal) trends. The focus is to understand the slowdown in the rate of warming since the late 1990s but earlier H/S events will also be considered. Previous investigations have largely focused on single mechanisms contributing to the current hiatus. Our approach advanceed current understanding and improve prediction of hiatus and surge decades by considering multiple mechanisms and their interactions. \r\n\r\nA novel, inter-disciplinary framework was applied to three primary research objectives. \r\nObjective 1: Developing a conceptual framework for characterising hiatus and surge events. There is currently no established framework for quantifying H/S events and their main causes. SMURPHS aims to understand the morphology of past events and develop new ways of comparing observations and models to understand interactions between different components of the climate system (atmosphere, land and ocean), forced response and internal variability. Specific objectives are; O1a. To create a suitable conceptual global framework to understand the role of interactions between forced response and internal variability. The framework would concentrate on how different processes can cause H/S events in global-mean surface temperature. O1b. To determine multivariate fingerprints of H/S events and their spatial structure in observations and models. There was a focus on the spatial structure of process variables relating to the flow of energy through the climate system. \r\nObjective 2: Assess the key processes in past hiatus and surge events. The project considered multiple potential mechanisms involved in driving, amplifying and extending H/S events with particular focus on four key processes and their interactions, that have been implicated in past events but have so far been poorly quantified. Specific objectives are; O2a. To understand the role of changing patterns of anthropogenic tropospheric aerosol emissions in driving and influencing the evolution of H/S events. O2b. To determine the influence of volcanic aerosol on H/S events for both large and small eruptions, quiet and active periods. O2c. To identify ocean dynamical mechanisms that alter ocean heat uptake (from the upper ocean, of about 100 m depth, which may be regarded as part of the surface climate system on interannual timescales, into the ocean beneath). O2d. To identify possible cloud feedback changes that alter the Earth's energy imbalance during H/S events. O2e. To understand how these possible drivers interact and may be linked. \r\nObjective 3: Determine the predictability of hiatus and surge events. The extent to which the recent hiatus was predictable is debated. Most hind-casts exhibit too much warming. This may be because the event was inherently unpredictable, or because model biases or inadequate observations preclude the ability to predict it. Specific objectives are: O3a. To assess the potential predictability of H/S events and to determine which processes are essential for predictability. A specific focus was on the role of volcanic eruptions. O3b. To assess the role of H/S events in near term future trends. This was aided by participation in international coordination efforts on decadal prediction. O3c. To quantify the contributions of forcing and internally generated variability to past H/S events O3d. 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MOCCHA aimed to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-lewel clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where e icebreaker Oden was moored to an ice flow and drifted passively with the ice." } ], "responsiblepartyinfo_set": [ 129535, 129536, 129537, 129538, 129539, 129540, 129542, 129541, 129543, 168363, 129544 ], "onlineresource_set": [] }, { "ob_id": 29978, "uuid": "47c32530265d4d6e8fdb6c08b2330371", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 Half Hourly 0.1 degree x 0.1 degree v6", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v6. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields. Level 3 data are averaged global gridded products, screened for bad data points\r\n\r\nThe Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2022-06-06T12:07:09", "updateFrequency": "unknown", "dataLineage": "Data taken as is from National Aeronautics and Space Administration (NASA). https://gpm1.gesdisc.eosdis.nasa.gov/data/GPM_L3/GPM_3IMERGHH.06/", "removedDataReason": "", "keywords": "GPM, IMERG, precipitation, global, retrievals", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-01-29T10:22:24", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 29979, "dataPath": "/badc/gpm/data/GPM-IMERG-v6/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 3999921915159, "numberOfFiles": 748059, "fileFormat": "Data are HDF5 formatted." }, "timePeriod": { "ob_id": 8155, "startTime": "2000-05-31T23:00:00", "endTime": null }, "resultQuality": { "ob_id": 3362, "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": "2019-12-10" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 29980, "uuid": "200b79c9798c49b7a9ef0c822add2013", "short_code": "comp", "title": "Integrated Multi-satellitE Retrievals for GPM (IMERG) v6", "abstract": "The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2017 version of the Goddard Profiling Algorithm (GPROF2017), then gridded, intercalibrated to the GPM Combined Ku Radar-Radiometer Algorithm (CORRA) product, and merged into half-hourly 0.1°x0.1° (roughly 10x10 km) fields." }, "procedureCompositeProcess": null, "imageDetails": [ 128 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2615, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 72, "licenceURL": "https://gpm.nasa.gov/data/policy", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26514, "uuid": "32b64be73ea2472c96dd5466b95480a4", "short_code": "proj", "title": "Global Precipitation Measurement (GPM)", "abstract": "The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow. The GPM mission is helping to advance our understanding of Earth's water and energy cycle, improve forecasting of extreme events that cause natural hazards and disasters, and extend current capabilities in using accurate and timely information of precipitation to directly benefit society." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29993, "uuid": "0a418abc19ee4700a49f9a86b519820f", "short_code": "coll", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG)", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v5 and v6. The Global Precipitation Measurement (GPM) mission is an international network of satellites that provide the next-generation global observations of rain and snow." } ], "responsiblepartyinfo_set": [ 129562, 129559, 129564, 129565, 129566, 129567, 129560, 129563, 129558, 168798 ], "onlineresource_set": [ 36784, 36783 ] }, { "ob_id": 29983, "uuid": "cc93b95c264644519777aa1ab37c23c0", "title": "Iceland Greenland Seas Project (IGP): three dimensional wind profile measurements from the University of Bergen Windcube V2 pulsed Lidar on board the NATO Research Vessel Alliance", "abstract": "This dataset contains corrected observations (motion and translational movement) from a WindCube V2 Lidar (Leosphere). The instrument was mounted on the NATO Research Vessel Alliance during Iceland Greenland Seas Project (IGP) campaign in February–March 2018.\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2019-12-12T11:44:37", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, processed, 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": "IGP, windcube, lidar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-05-11T12:34:54", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2570, "bboxName": "", "eastBoundLongitude": 0.0, "westBoundLongitude": -30.0, "southBoundLatitude": 64.0, "northBoundLatitude": 76.0 }, "verticalExtent": null, "result_field": { "ob_id": 29984, "dataPath": "/badc/igp/data/windcube/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 42436428, "numberOfFiles": 37, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8151, "startTime": "2018-02-14T00:00:00", "endTime": "2018-03-21T23:59:59" }, "resultQuality": { "ob_id": 3364, "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": "2019-12-12" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 29985, "uuid": "e94feeab305744b987ac6a4fdd203dc6", "short_code": "acq", "title": "Acquisition for: Iceland Greenland Seas Project (IGP): three dimensional wind profile measurements from the University of Bergen Windcube V2 pulsed Lidar on board the NATO Research Vessel Alliance", "abstract": "Acquisition for: Iceland Greenland Seas Project (IGP): three dimensional wind profile measurements from the University of Bergen Windcube V2 pulsed Lidar on board the NATO Research Vessel Alliance" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2543, "accessConstraints": null, "accessCategory": "registered", "accessRoles": null, "label": "registered: None group", "licence": { "ob_id": 2, "licenceURL": "https://artefacts.ceda.ac.uk/licences/missing_licence.pdf", "licenceClassifications": [ { "ob_id": 2, "classification": "unstated" } ] } } ], "projects": [ { "ob_id": 24899, "uuid": "2780d047461c42f0a12534ccf42f487a", "short_code": "proj", "title": "Iceland Greenland seas Project (IGP) including the Atmospheric Forcing of the Iceland Sea (AFIS)", "abstract": "The Iceland Greenland seas Project (IGP) is an international project involving the UK, US a Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)\r\n\r\nThe Iceland Sea - to the north and east of Iceland - is arguably the least studied of the North Atlantic's subpolar seas. However new discoveries are forcing a redesign of our conceptual model of the North Atlantic's ocean circulation which places the Iceland Sea at the heart of this system and suggests that it requires urgent scientific focus. The recently discovered North Icelandic Jet is thought to be one of two pathways for dense water to pass through the Denmark Strait - the stretch of ocean between Iceland and Greenland - which is the main route for dense waters from the north to enter the Atlantic. Its discovery suggests a new paradigm for where dense water entering the North Atlantic originates. However at present the source of the North Icelandic Jet remains unknown. It is hypothesized that relatively warm Atlantic-origin water is modified into denser water in the Iceland Sea, although it is unclear precisely where, when or how this happens. \r\n\r\nThis project examined the wintertime atmosphere-ocean processes in the Iceland Sea by characterising its atmospheric forcing, i.e. observing the spatial structure and variability of surface heat, moisture and momentum fluxes in the region and the weather systems that dictate these fluxes. In situ observations of air-sea interaction processes from several platforms (an aircraft; and via project partners an unmanned airborne vehicle, a meteorological buoy and a research vessel) were made and used to evaluate meteorological analyses and reanalyses from operational weather forecasting centres. \r\n\r\nNumerical modelling experiments investigated the dynamics of selected weather systems which strongly influenced the region, but appear not to be well represented; for example, the boundary layers that develop over transitions between sea ice and the open ocean during cold-air outbreaks; or the jets and wakes that occur downstream of Iceland. The unique observations were used to improve model representation of these systems.\r\n\r\nThe project also carried out new high-resolution climate simulations. A series of experiments covered recent past and likely future situations; as well as some idealised situations such as no wintertime sea ice in the Iceland Sea region. This was done using a state-of-the-art atmospheric model with high resolution over the Iceland Sea to investigate changes in the atmospheric circulation and surface fluxes. \r\n\r\nFinally, in collaboration with the international partners, the project analysed new ocean observations and establish which weather systems are important for changing ocean properties in this region. The project used a range of ocean and atmospheric models to establish how current and future ocean circulation pathways function. In short, the project determined the role that atmosphere-ocean processes in the Iceland Sea play in creating the dense waters that flow through Denmark Strait and feed into the lower limb of the AMOC.\r\n\r\nThe subpolar region of the North Atlantic is crucial for the global climate system. It is where coupled atmosphere-ocean processes, on a variety of spatial scales, require an integrated approach for their improved understanding and prediction. This region has enhanced 'communication' between the atmosphere and ocean. Here large surface fluxes of heat and moisture make the surface waters colder, saltier and denser resulting in a convective overturning that contributes to the lower limb of the Atlantic Meridional Overturning Circulation (AMOC). The AMOC is an ocean circulation that carries warm water from the tropics northward with a return flow of cold water southwards at depth; it is instrumental in keeping Europe's climate relatively mild." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 51981, 68600, 68601, 68602, 68603, 68604, 68605, 68606, 68607, 68608, 68609, 68610, 68611, 68612, 68613, 68614, 68615, 68616, 68617, 68618 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 27445, "uuid": "b3e807b8df824a8ca83468ce2e5b54e5", "short_code": "coll", "title": "In situ observations of air-sea interaction processes from the Iceland Greenland seas Project (IGP)", "abstract": "This collection contains a range of in situ observations of meteorological and air-sea interaction processes from a range of instruments on several platforms (buoy, ship , radiosonde) from the Iceland Greenland seas Project (IGP). \r\n\r\n\r\nThe Iceland Greenland seas Project (IGP) was an international project involving the UK, US and Norwegian research communities. The UK component was funded by NERC, under the Atmospheric Forcing of the Iceland Sea (AFIS) project (NE/N009754/1)" } ], "responsiblepartyinfo_set": [ 129572, 129573, 129574, 129575, 129576, 129577, 129579, 129578 ], "onlineresource_set": [] }, { "ob_id": 29988, "uuid": "52cda2e0e6c04272ae15ac836c1e8493", "title": "LiCSAR interferometry products", "abstract": "This dataset contains Interferometric Synthetic Aperture Radar (InSAR) products generated by Looking inside the Continents from Space Synthetic Aperture Radar (LiCSAR) from Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). Interferograms and coherence maps have been produced automatically using the LiCSAR processor, which builds on the Gamma SAR and Interferometry software. Interferograms have been processed in overlapping “frames” defined within COMET-LiCS.\r\n\r\nThe basic products are given in the list below:\r\n\r\n• geo.cc.tif: This is the coherence image of the interferometric pair. The values vary between 0-255 in which 0 refers to the lowest coherence values and 255 indicates the highest values of coherence.\r\n\r\n• geo.diff_pha.tif: The wrapped phase image in radian. The values vary within the range of -3.14 to 3.14. These are differential phases as the topographic correction has already been applied using 1 arc-second SRTM DEM data. \r\n\r\n• geo.unw.tif: This is the unwrapped phase image in radian. The unwrapping is performed using SNAPHU. The zero values in the unwrapped file refer to the pixels which are masked out due to the low coherence. \r\n\r\nAll products are georeferenced to WGS-84 geographic coordinate system. \r\n\r\nLiCSAR products are categorised into 175 folders which correspond to the Sentinel-1 175 orbits per cycle which are the same for both Sentinel-1A and Sentinel-1B. In the next level, the frames are defined. The frames are slices that refer to the same point temporally and geographically. The name of each frame (OOOP_AAAAA_BBBBBB) consists of 3 parts: in the first part, the three digits OOO denotes the orbit number while P shows whether it is descending (D) or ascending (A). The next part i.e. AAAAA shows the geographic location of the frame. Finally, the last part BBBBBB identifies the number of bursts where the first, second and third digit pairs identify the number of bursts in the first, second and third sub-swath respectively.\r\n\r\nThe LiCSAR processing is currently generating 3 connections per epoch. This means that for each epoch there are 3 interferometric pairs generated using the next 3 chronological acquisitions.\r\n\r\nPlease also include the following acknowledgement and citations when using LiCSAR data:\r\n\r\nAcknowledgement: “LiCSAR contains modified Copernicus Sentinel data [Year of data used] analysed by the Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET). LiCSAR uses JASMIN, the UK’s collaborative data analysis environment (http://jasmin.ac.uk)”", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-14T10:26:44", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "COMET, Sentinel 1", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-11-15T15:27:24", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 3669, "bboxName": "COMET LiCSAR", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -68.5, "northBoundLatitude": 72.0 }, "verticalExtent": null, "result_field": { "ob_id": 29989, "dataPath": "/neodc/comet/data/licsar_products/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 136103081011288, "numberOfFiles": 13568552, "fileFormat": "Data are supplied in Tiff format with corresponding .png image" }, "timePeriod": { "ob_id": 10748, "startTime": "2014-10-03T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 4108, "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": "2022-11-08" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 38860, "uuid": "69d72964a30c421392a4fcdd3074ddd9", "short_code": "cmppr", "title": "Composite process for: LiCSAR interferometry products", "abstract": "Processing Sentinel 1 IW SLC products using the LiCSAR processing chain." }, "imageDetails": [ 222 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 11687, "uuid": "b46fbc668f6547fda79f2899046c29a9", "short_code": "proj", "title": "Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics", "abstract": "The Centre for the Observation and Modelling of Earthquakes, Volcanoes and Tectonics (COMET+) represents the Dynamic Earth and Geohazards research group within the National Centre for Earth Observation (NCEO)'s Theme 6 during NCEO phase 1. NCEO phase 1 was is funded by the Natural Environment Research Council (NERC). NCEO phase 2 no longer has the theme 6 within its remit, though COMT+ continues within NERC.\r\n\r\nCOMET+ involves scientists from the University of Oxford, University of Cambridge, University of Leeds, University of Bristol, University oSf Glasgow, University of Reading, and University College London. We aim to combine satellite observations of Earth's surface movements, topography and gas release with terrestrial observations and modelling to advance understanding of the earthquake cycle, continental deformation and volcanic eruptions, and to quantify seismic and volcanic hazards." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 10621, 50559, 50561, 68619, 68620, 68621 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 129594, 129592, 129595, 141791, 141792, 141793, 141794, 129596, 129593 ], "onlineresource_set": [ 80231, 80232, 80233, 80234 ] }, { "ob_id": 29992, "uuid": "f717a8ea622f495397f4e76f777349d1", "title": "STFC RAL methane retrievals from IASI on board MetOp-A, version 2.0", "abstract": "This Infrared Atmospheric Sounding Interferometer (IASI) methane dataset contains height-resolved and column-averaged volume mixing ratios of atmospheric methane (CH4). It also includes column-averaged water vapour (H2O), a scale factor for the HDO (water vapour isotopologue) volume mixing ratio profile, surface temperature, effective cloud fraction, effective cloud-top pressure and scale factors for two systematic residual spectra which are jointly retrieved from the spectral range 1232.25-1290.00 cm-1 by the Rutherford Appleton Laboratory (RAL) IASI optimal estimation methane retrieval scheme. The dataset additionally contains selected a priori values and uncertainties adopted in the optimal estimation scheme and retrieval output diagnostics such as the retrieval cost and the averaging kernels.\r\n\r\nThis work was funded by the National Centre for Earth Observation (NCEO) under the UK Natural Environment Research Council (NERC) with additional funding from EUMETSAT.\r\n\r\nData were produced by the United Kingdom Research and Innnovation (UKRI) Science and Technology Facilities Council (STFC) Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).\r\n\r\nThis is version 2.0 of the dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-24T13:55:38.118010", "updateFrequency": "notPlanned", "dataLineage": "Data produced by the Rutherford Appleton Laboratory (RAL) Remote Sensing Group (RSG) at the United Kingdom Research and Innovation (UKRI) Science and Technology Facilities Council (STFC). The development of the Infrared Atmospheric Sounding Interferometer (IASI) offline thermal-IR methane retrieval was funded by the Natural Environment Research Council (NERC) through its National Centre for Earth Observation (NCEO) with additional funding from EUMETSAT.", "removedDataReason": "", "keywords": "methane, IASI, optimal estimation, retrieval, averaging kernels, assimilation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-25T13:35:49", "doiPublishedTime": "2020-03-10T14:06:11.939534", "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": 30119, "dataPath": "/neodc/iasi_ch4_ral/data/metop-a/l2/tir/v2.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1148805035519, "numberOfFiles": 828516, "fileFormat": "Data are in NetCDF format." }, "timePeriod": { "ob_id": 8153, "startTime": "2007-05-31T23:00:00", "endTime": "2017-12-31T23:59:59" }, "resultQuality": { "ob_id": 3365, "explanation": "Data are as provided by the data providers. 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The dataset also includes column-averaged water vapour (H2O), a scale factor for the HDO (water vapour isotopologue) volume mixing ratio profile, surface temperature, effective cloud fraction, effective cloud-top pressure and scale factors for two systematic residual spectra which are jointly retrieved from the spectral range 1232.25-1290.00 cm-1 by the Rutherford Appleton Laboratory (RAL) IASI optimal estimation methane retrieval scheme. The dataset also contains selected a priori values and uncertainties adopted in the optimal estimation scheme and retrieval output diagnostics such as the retrieval cost and the averaging kernels. \r\n\r\nData were produced by the United Kingdom Research and Innovation (UKRI) Science and Technology Facilities Council (STFC) Remote Sensing Group (RSG) at the Rutherford Appleton Laboratory (RAL).\r\n\r\nThe development of the STFC RAL methane retrieval was funded by the Natural Environment Research Council (NERC) through its National Centre for Earth Observation (NCEO) with additional funding from EUMETSAT." }, { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 129610, 129601, 129602, 129603, 129604, 129606, 129607, 129605, 129611, 129612, 129613, 129614 ], "onlineresource_set": [ 36929, 36807, 87759 ] }, { "ob_id": 29994, "uuid": "7dc67a6d16b245e3bd56fb05ecf449a1", "title": "Si-SOA: Concentration of specific ions found in PM2.5 samples taken from the Institute of Atmospheric Physics Beijing site in August 2018 and January 2019", "abstract": "This dataset contains both the concentration of PM2.5 in ambient air sampled at a height of 8 metres and the concentration of specific ions found in the PM2.5 samples resulting from the analysis by Ion Chromatography. The PM2.5 samples were collected in the Institute of Atmospheric Physics, Chinese Academy of Sciences, China in August 2018 and January 2019. \r\n\r\nThe data was collected to support the study of atmospheric chemistry and processes involving Silicon (Si) containing fine particles, which potentially contribute to atmospheric pollution.\r\n\r\nThese data were collected as part of the Silicon-containing secondary organic aerosols in ambient air (Si-SOA) Natural Environment Research Council (NERC) funded project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-27T10:25:38", "updateFrequency": "notPlanned", "dataLineage": "Data produced by University of Birmingham and uploaded to Centre for Environmental Data Anaylsis (CEDA) for archiving.", "removedDataReason": "", "keywords": "SI-SOA, PM2.5, ions, chromatography", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-18T09:06:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2572, "bboxName": "", "eastBoundLongitude": 116.371, "westBoundLongitude": 116.371, "southBoundLatitude": 39.974, "northBoundLatitude": 39.974 }, "verticalExtent": null, "result_field": { "ob_id": 29995, "dataPath": "/badc/deposited2020/si-soa/data/ion_chromatography/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6501, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 8156, "startTime": "2018-08-07T23:00:00", "endTime": "2019-01-22T00:00:00" }, "resultQuality": { "ob_id": 3366, "explanation": "Data is as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 29997, "uuid": "6cf21f0e930f45d6b53d15fcf9902d46", "short_code": "acq", "title": "Acquisition for: Si-SOA study_inorganic ions data set 2018-2019", "abstract": "Acquisition for: Si-SOA study_inorganic ions data set 2018-2019" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 29996, "uuid": "cb3943b3fd3e41618e2c1283d6b154f6", "short_code": "proj", "title": "Silicon-containing secondary organic aerosols in ambient air (Si-SOA)", "abstract": "The Si-SOA project was lead by the University of Birmingham and ran from April 2018 - June 2019. The project aimed to advance the understanding of an important aspect of air pollution - formation of silicon-organic aerosols via atmospheric oxidation of silicon-containing volatile organic compounds from the personal care products and industries. The project sought to provide definitive evidence on the formation of silicon-containing secondary organic aerosols (Si-SOAs) via photochemical atmospheric processing by using the novel techniques at the Institute of Atmospheric Physics and the University of Birmingham. \r\n\r\nProject outputs will be of interest to research scientists working in the field of atmospheric chemical processing and human health. The results of the project support the study of the oxidation of Si-containing volatile organic compounds (Si-VOCs), which are widely used in personal care products and industrial applications. Si-VOCs are the most abundant VOCs in indoor air and its concentration in Chinese megacities can be over 10 microgram per cubic meter (which is extremely high). Si-VOCs can be oxidised by hydroxyl radicals and form secondary organic aerosols (SOAs), which contributes to the regional haze pollution." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65473, 65484, 65490, 65491, 67359, 67360, 67361, 67362, 67363, 67364, 67365, 67366, 67367 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30041, "uuid": "14f16b7ee7f24f3992e84c22dfb736ca", "short_code": "coll", "title": "PM2.5 analysis results of samples taken from the Institute of Atmospheric Physics -Beijing site by the Si-SOA project", "abstract": "This dataset collection contains the results of the analysis conducted on PM2.5 (particulate matter) samples by the Si-SOA project. The PM2.5 samples were collected in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, China during August 2018 and January 2019 by the Silicon-containing secondary organic aerosols in ambient air (Si-SOA) project. \r\n\r\nThe PM2.5 samples were taken from ambient air at the height of 8m and subjected to a series of analytical techniques, and the data collection is comprised of the following results. \r\n\r\n- The concentration of water-soluble Silicon/water-soluble organic Silicon/water-soluble inorganic Silicon in PM2.5 samples from Ultraviolet-Visible Spectrophotometry\r\n- The concentration of specific ions in PM2.5 samples from Ion Chromatography\r\n- The concentration of water-soluble Silicon in PM2.5 samples from Coupled Plasma - Optical Emission Spectrometry (ICP-OES)\r\n- The concentration of water-soluble elements in PM2.5 samples from Inductively Coupled Plasma Mass Spectrometry (ICP-MS)\r\n- The concentration of elements in PM2.5 samples from X-ray Fluorescence Spectrometry\r\n\r\nThese data support the study of atmospheric processes in relation to fine Silicon-containing particles, which may contribute to the formation of haze and atmospheric pollution." } ], "responsiblepartyinfo_set": [ 129638, 129640, 129639, 129633, 129634, 129635, 129636, 129637, 129641, 168411, 129642, 129643 ], "onlineresource_set": [ 36918, 36919 ] }, { "ob_id": 29998, "uuid": "fa20aaa2060e40cabf5fedce7a9716d0", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 – 2018), version 1.0", "abstract": "Snow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces; in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979 to 2018. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. \r\n\r\nThe product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.\r\n\r\nThe dataset was aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product development and generation. For the period from 1979 to May 1987, the products are available every second day. From October 1987 till May 2018, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2020-04-03T15:46:03", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "ESA, CCI, Snow, SWE", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2020-04-20T11:19:31", "doiPublishedTime": "2020-04-20T13:30:10", "removedDataTime": null, "geographicExtent": { "ob_id": 2614, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30228, "dataPath": "/neodc/esacci/snow/data/swe/MERGED/v1.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2765197518, "numberOfFiles": 7398, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8223, "startTime": "1979-01-06T00:00:00", "endTime": "2018-05-31T23:59:59" }, "resultQuality": { "ob_id": 3402, "explanation": "Data are as provided by the data producer. The statistical standard deviation of snow water equivalent is added as uncertainty layer in each product.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-04-06" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30230, "uuid": "97f53774444443b9a49a9e85dfb6a971", "short_code": "comp", "title": "ESA Snow Climate Change Initiative (snow_cci): SWE", "abstract": "The snow_cci SWE product has been based on the ESA GlobSnow SWE retrieval approach (Takala et al. 2011). The retrieval is based on passive microwave radiometer (PMR) data considering the change of brightness temperature due to different snow depth, snow density, grain size and more. The retrieval algorithm handles data from the sensors SMMR, SSM/I, SSMIS, AMSR-E and AMSR-2. The retrieval methodology combines the satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme. Improvements to the GlobSnow algorithm implemented for snow_cci version 1 include the utilisation of an advanced emission model with an improved forest transmissivity module and treatment of sub-grid lake ice. Because of the importance of the weather station snow-depth observations on the SWE retrieval, there is improved screening for consistency through the time series.\r\n\r\nSnow_cci SWE version 1products are based on SMMR, SSM/I and SSMIS PMR data for non-alpine regions of the Northern Hemisphere." }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2571, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 38, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_snow_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30229, "uuid": "93cf539bc3004cc8b98006e69078d86b", "short_code": "proj", "title": "ESA Snow Climate Change Initiative (snow_cci)", "abstract": "The overarching goal of snow_cci is the generation of homogeneous, well calibrated, long-term time series of key snow cover parameters (snow area extent and snow mass) from multi-sensor satellite data for climate applications. A main motivation for this initiative are significant discrepancies in the climatologies, anomalies, and trends in global snow cover time series from different products, detected in the ESA QA4EO Satellite Snow Product Intercomparison and Evaluation project (SnowPEx).\r\n\r\nThe first phase of the project started in September 2018; the second phase started in February 2022. The third phase of the snow_cci project is planned to start in late 2025." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6021, 6022, 28682, 28683, 28684, 28685, 28686 ], "vocabularyKeywords": [ { "ob_id": 11058, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_snow", "resolvedTerm": "snow" }, { "ob_id": 11123, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_smmr", "resolvedTerm": "SMMR" }, { "ob_id": 11126, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_ssmI", "resolvedTerm": "SSM/I" }, { "ob_id": 11127, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_ssmiS", "resolvedTerm": "SSMI/S" }, { "ob_id": 10795, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_dmsp_F08", "resolvedTerm": "DMSP-F08" }, { "ob_id": 10798, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_dmsp_F11", "resolvedTerm": "DMSP-F11" }, { "ob_id": 10800, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_dmsp_F13", "resolvedTerm": "DMSP-F13" }, { "ob_id": 10804, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_dmsp_F17", "resolvedTerm": "DMSP-F17" }, { "ob_id": 10882, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_nimbus7", "resolvedTerm": "Nimbus-7" } ], "identifier_set": [ 10712 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130631, 130632, 130640, 130641, 130643, 130644, 130645, 130642, 130633, 130634, 130635, 130636, 130690, 130637, 130638, 130639 ], "onlineresource_set": [ 37081, 37082, 37083, 37084, 37085, 41660, 41954, 41955, 92701, 94663, 95056 ] }, { "ob_id": 29999, "uuid": "fa5f38faa9304e7996aed11522cde99b", "title": "TOMCAT CTM Global Ozone Simulations", "abstract": "Global 3-D simulations of tropospheric ozone, using the TOMCAT chemical transport model, were undertaken as part of the NERC - National Centre for Earth Observation (NCEO) long-term science activities. The work investigated long-term trends in air pollutants and fire emissions observed/derived from space over the North-eastern Amazon region and used the TOMCAT CTM to interpret the impact on surface-level ozone concentrations, given the limited observational Spatio-temporal coverage. The model setup is based on that of Monks et al., (2017), but uses annually varying emissions as discussed in Pope et al., (2019).. The ozone data is provided as 6-hourly 3-D fields on the (approximately) 2.8 degree horizontal model grid. Atmospheric pressure is also included to provide vertical information on the ozone distribution up to 10 hPa. The high temporal output of the model is to allow for the accurate comparison of model simulations with satellite observations of atmospheric composition. While the runs by Pope et al., (2019) cover 2005 to 2016, only 2010 has been uploaded so far. The ozone data has units of volume mixing ratio (dimensionless), pressure data is in Pa. Lon, lat, time and vertical information is also included.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-13T14:47:28", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "TOMCAT", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2575, "bboxName": "TOMCAT Ozone", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": null, "resultQuality": null, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [], "projects": [], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 204998, 204999, 205000, 205001, 205002, 205003 ], "onlineresource_set": [ 36808, 36809 ] }, { "ob_id": 30000, "uuid": "e488dccd09e1446d90978b75036475e2", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2019, v3.1.0.2019f", "abstract": "This is version 3.1.0.2019f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.0.0.2018f to include 2019 and so spans 1931-2019.\r\n\r\nThe quality controlled variables in this dataset are: temperature, dewpoint temperature, sea-level pressure, wind speed and direction, cloud data (total, low, mid and high level). Past significant weather and precipitation data are also included, but have not been quality controlled, so their quality and completeness cannot be guaranteed. Quality control flags and data values which have been removed during the quality control process are provided in the qc_flags and flagged_values fields, and ancillary data files show the station listing with a station listing with IDs, names and location information. \r\n\r\nThe data are provided as one NetCDF file per station. Files in the station_data folder station data files have the format \"station_code\"_HadISD_HadOBS_19310101-20200101_v3-1-0-2019f.nc. The station codes can be found under the docs tab or on the archive beside the station_data folder. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T02:30:34", "updateFrequency": "notPlanned", "dataLineage": "HadISD the global sub-daily station dataset was produced by the Met Office Hadley Centre. It was derived from the Integrated Surface Dataset (ISD) from NOAA's National Climatic Data Center (NCDC). HadISD has been passed to the Centre for Environmental Data Analysis (CEDA) for archiving and distribution.", "removedDataReason": "", "keywords": "HadISD, temperature, dewpoint, pressure, wind, speed, direction, cloud, precipitation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2020-01-16T15:38:26", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30001, "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISD/subdaily/HadISDTable/r1/v3-1-0-2019f", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 44713429386, "numberOfFiles": 8153, "fileFormat": "The data are NetCDF formatted." }, "timePeriod": { "ob_id": 8158, "startTime": "1931-01-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3104, "explanation": "CF-Compliant NetCDF, see documentation for quality control processes used to produce these data. These data are quality controlled by the data provider, the Met Office Hadley Centre (MOHC) and not the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-02-19" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 20043, "uuid": "32eff53af32442d1a347da2cc45bb9db", "short_code": "comp", "title": "HadISD station data processing performed at the Met Office Hadley Centre", "abstract": "The HadISD station data were produced by the Met Office Hadley Centre. Individual station data within the ISD were selected selected on the basis of their length of record and reporting frequency. A merging algorithm using their location, elevation and station name identified candidates suitable to combine together. All stations were passed through a suite of automated quality control tests designed to remove bad data whilst keeping the extremes. None of the ISD flags were used in this process. The QC tests focussed on the temperature, dewpoint temperature and sea-level pressure variables, although some were applied to the wind speed and direction and cloud data. The data files also contain other variables which were pulled through from the raw ISD record, but have had no QC applied (e.g. cloud base and precipitation depth). \r\n\r\nNotes:\r\n1. These data have not yet been homogenised and so trend fitting should be undertaken with caution. The homogeneity has been assessed and results are available from the Met Office Hadley Centre HadISD website: http://www.metoffice.gov.uk/hadobs/hadisd/. \r\n2. A long-standing bug (affecting versions v2.0.2_2017p through to v3.3.0.2022f), was discovered in autumn 2023 whereby the neighbour checks (and associated [un]flagging for some other tests) were not being implemented. This was corrected for the later version v3.4.0.2023f to HadISD. For more details see the posts on the HadISD blog: https://hadisd.blogspot.com/2023/10/bug-in-buddy-checks.html & https://hadisd.blogspot.com/2024/01/hadisd-v3402023f-future-look.html(v2.0.2_2017p through to v3.3.0.2022f), and as noted this has been fixed for v3.4.02023f.\r\n\r\n\r\nFor further details see: \r\nDunn, R. J. H., et al., (2016), Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, and Dunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations\r\nfrom 1973-2011, Climate of the Past.\r\nDunn, R. J. H., et al. 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Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2019-10-04T14:06:15", "updateFrequency": "notPlanned", "dataLineage": "Data was produced by the ESA Sea State CCI team at Ifremer and was transferred to CEDA as part of the ESA CCI Open Data Portal project.", "removedDataReason": "", "keywords": "Sea State, CCI, Significant Wave Height", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2020-01-30T16:47:40", "doiPublishedTime": "2020-01-30T17:35:20", "removedDataTime": null, "geographicExtent": { "ob_id": 2578, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -80.0, "northBoundLatitude": 80.0 }, "verticalExtent": null, "result_field": { "ob_id": 30012, "dataPath": "/neodc/esacci/sea_state/data/v1.1_release/l4/v1.1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 517839020, "numberOfFiles": 330, "fileFormat": "Data are in NetCDF format" }, "timePeriod": { "ob_id": 8161, "startTime": "1991-07-31T23:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3368, "explanation": "This product provides an average and other statistical moments of sea wave height computed over the L2P products whose data were quality checked and compared to in situ reference data. For more details on the validation process, refer to the product user guide.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-20" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2604, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 63, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_sea_state_terms_and_conditions.pdf", "licenceClassifications": [] } } ], "projects": [ { "ob_id": 30004, "uuid": "7cfcd20428c3454fafa4e1afec2cf923", "short_code": "proj", "title": "ESA Sea State Climate Change Initiative Project", "abstract": "The European Space Agency (ESA) Sea State CCI+ project is part of ESA's Climate Change Initiative (CCI) programme. The CCI programme was launched by ESA in 2010, to produce long term datasets of Essential Climate Variables (ECV's) derived from global satellite data. In this context, the Sea State CCI+ project was kicked off in 2018 in order to produce a CDR for the new ECV \"Sea State\"." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 6019, 6020, 6021, 6022, 6023, 12066, 28342, 28343, 28344, 28345, 28346, 28347, 28348, 28349, 28350, 28351, 28352, 28353, 28354, 28355, 28356, 28357, 28358, 28359, 28360, 28361 ], "vocabularyKeywords": [ { "ob_id": 11062, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_seaState", "resolvedTerm": "sea state" }, { "ob_id": 11046, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_altika", "resolvedTerm": "AltiKa" }, { "ob_id": 11093, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_gfora", "resolvedTerm": "GFO-RA" }, { "ob_id": 11148, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_nra", "resolvedTerm": "NRA" }, { "ob_id": 11113, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon2", "resolvedTerm": "Poseidon-2" }, { "ob_id": 11114, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon3", "resolvedTerm": "Poseidon-3" }, { "ob_id": 11115, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_poseidon3b", "resolvedTerm": "Poseidon-3B" }, { "ob_id": 11116, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_ra", "resolvedTerm": "RA" }, { "ob_id": 11117, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_ra2", "resolvedTerm": "RA-2" }, { "ob_id": 11055, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_siral", "resolvedTerm": "SIRAL" }, { "ob_id": 10787, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_cryosat2", "resolvedTerm": "CryoSat-2" }, { "ob_id": 10809, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_ers1", "resolvedTerm": "ERS-1" }, { "ob_id": 10810, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_ers2", "resolvedTerm": "ERS-2" }, { "ob_id": 10808, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_envisat", "resolvedTerm": "Envisat" }, { "ob_id": 10815, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_gfo", "resolvedTerm": "GFO" }, { "ob_id": 10846, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_jason1", "resolvedTerm": "Jason-1" }, { "ob_id": 10847, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_jason2", "resolvedTerm": "Jason-2" }, { "ob_id": 10848, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.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": 10926, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_topexPoseidon", "resolvedTerm": "Topex/Poseidon" } ], "identifier_set": [ 10693 ], "observationcollection_set": [ { "ob_id": 30025, "uuid": "c0edac5a52c64e55ab61bc49a493ef9c", "short_code": "coll", "title": "ESA Sea State Climate Change Initiative (Sea_State_cci): Version 1.1 data collection", "abstract": "The ESA Sea State CCI project has produced global multi-sensor time-series of satellite altimeter significant wave height data with a particular focus for use in climate studies. A number of products are available: a Level 2P (L2P) along-track dataset, a Level 3 (L3) daily merged along-track dataset, and a L4 montly gridded product.\r\n\r\nThis first version of the Sea State CCI products is inherited from the GlobWave project, building on experience and existing outputs. It extends and improves the GlobWave products ,which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition, which was used as input to these monthly statistical fields.\r\n\r\nThe altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band)." } ], "responsiblepartyinfo_set": [ 129682, 129683, 129684, 129686, 129680, 141376, 141377, 129685, 129679, 129687, 129681 ], "onlineresource_set": [ 36824, 36823, 36894, 91981, 91982, 91983, 91984, 91985, 91986, 91987, 91988 ] }, { "ob_id": 30026, "uuid": "14a35bf86fe54fc5ae78903691495c52", "title": "VolcanEESM: Global volcanic sulphur dioxide (SO2) emissions database from 1850 to present - Version 2.0", "abstract": "This dataset is associated with the VolcanEESM project led by the project team at the University of Leeds. The project was funded by NCAR/UCAR Atmospheric Chemistry and Modeling Visiting Scientist Program, NCAS, University of Leeds. \r\n\r\nThe global volcanic sulphur dioxide (SO2) emissions database is a combination of available information from the wider literature with as many observations of the amount and location of SO2 emitted by each volcanic eruption as possible. The database includes no information about the size, mass, distribution or optical depth of resulting aerosol. As such the database is model agnostic and it is up to each modeling group to make decisions about how to implement the emission file in their prognostic stratospheric aerosol scheme. \r\n\r\nThe dataset is divided into two parts based on the availability of satellite data. For the pre-satellite era, the necessary information about the emissions was gathered from the latest ice core records of sulphate deposition in combination historical accounts available in the wider literature (see references included in the database for specific citation for each record). In the satellite era, volcanic emissions were primarily derived from remotely sensed observations. \r\n\r\nFor the period 1850 CE to 1979 the dataset combined the most recent volcanic sulfate deposition datasets from ice cores with volcanological and, where applicable, petrological estimates of the SO2 mass emitted as well as historical records of large-magnitude volcanic eruptions. In detail, for the majority of eruptions between 1850 CE to 1979 , there are few direct measurement of SO2 emissions or quantitative observations of the plume height and very few measurements of the aerosol optical depth (AOD). \r\n\r\nParameters in the database include: \r\nDay_of_Emission: The 24 hour period in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nEruption: Field that contains the Volcano_Number (Which uniquely identifies each volcano in the Global Volcanism Program Database), Volcano_Name (official name from the Global Volcanism Program Database), Notes_and_References (list of notes about the observed parameters and references used to derive each entry). ( Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nLatitude: Latitude of each emission from -90 to +90 (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nLongitude: Longitude of each emission degrees East (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nVEI: Volcanic Explosively Index of each emission based on Global Volcanism Program Database (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nTotal_Emission_of_SO2_Tg: Total emission of SO2 in teragram for the specific database entry (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nMaximum_Injection_Height_km: Maximum height of each emission in kilometers above sea level. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nMinimum_Injection_Height_km: Minimum height of each emission in kilometers above sea level. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nMonth_of_Emission: The month in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)\r\n\r\nYear_of_Emission: The Year in which the emission is thought to have occurred. (Ordered by the variable Eruption_Number starting with the first eruption in the database.)", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": null, "updateFrequency": "notPlanned", "dataLineage": "Data provided by the project PI for archival at CEDA.", "removedDataReason": "", "keywords": "Volcano, SO2, sulphur dioxide, volcanic eruptions, Earth System Models, VolcanEESM", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": null, "timePeriod": { "ob_id": 3679, "startTime": "1850-01-01T00:00:00", "endTime": "2015-04-23T23:00:00" }, "resultQuality": { "ob_id": 3369, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-27" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 13714, "uuid": "59e56d65efc743249226fd5c52e05fe1", "short_code": "acq", "title": "VolcanEESM: collection of SO2 emissions collated from wider literature and observations", "abstract": "The data were collated by the project team from a wide variety of literature, see files for more detailed information. " }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [], "permissions": [], "projects": [ { "ob_id": 13700, "uuid": "fab717b3cf06461490b186fe5242c45d", "short_code": "proj", "title": "VolcanEESM (Volcanic Emissions for Earth System Models)", "abstract": "Volcanic eruptions impact the Earth system and society on timescales of weather (days to weeks) to climate (months to years) and are the dominant driver of climate variability over the last millennium. However, volcanic eruptions are most often poorly represented in Earth system models. This project was funded by: NCAR/UCAR Atmospheric Chemistry and Modeling Visiting Scientist Program, NCAS, University of Leeds School of Earth and Environment. \r\n\r\nThe VolcanEESM project presents a volcanic sulphur dioxide (SO2) emissions database for use in Earth System models. The database provides a comprehensive list of volcanic eruptions that were reported to emitted significant amounts of SO2 from 1850 to present. The presented dataset is instrumental for research using Earth system models that that represent stratospheric aerosols prognostically, including those participating in forthcoming Climate Modelling Inter-comparison Projects. \r\n\r\nThe project focused on large-magnitude (VEI>4) eruptions, which was used to identify using the Smithsonian Global Volcanism Program database and ice-core data. The total amount of SO2 emitted by an eruption, including the spatial distribution of the volcanic injection, was determined by using parameters that based on analogous eruptions that occurred during the satellite era following the methodology of Stoffel et al. [2015] and Gao et al. [2008]. \r\n\r\nThe project estimated lower and upper bounds of the SO2 emitted and constrained the season by an eruption [similar to Stoffel et al., 2015] and expect the uncertainties on our inventory to be of comparable magnitude to those prescribing the forcing. Before running long model integrations, they assessed the representativeness of the emission inventory and quantified uncertainties by comparing simulated sulphate deposition patterns for selected eruptions to ice-core records and by comparing simulated AOD to Crowley et al. [2008] and Sato et al. [1993]. Where applicable they also compared simulated optical properties to astronomical estimates of atmospheric transmission [Stothers 1996; 2001]. " } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 129757, 129758, 129751, 129752, 129754, 129755, 129756, 129753, 129759 ], "onlineresource_set": [] }, { "ob_id": 30027, "uuid": "7abc197a062a448bb32a76d6f3371c66", "title": "Si-SOA: Concentration of water-soluble elements found in PM2.5 samples taken from Institue of Atmospheric Physics Beijing site in August 2018 and January 2019", "abstract": "This dataset contains both the concentration of PM2.5 in ambient air sampled at a height of 8 metres and the concentration of water-soluble elements found in the PM2.5 samples resulting from the analysis by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The PM2.5 samples were collected in the Institute of Atmospheric Physics, Chinese Academy of Sciences, China in August 2018 and January 2019. \r\n\r\nThe data was collected to support the study of atmospheric chemistry and processes involving Silicon (Si) containing fine particles, which potentially contribute to atmospheric pollution\r\n\r\nThese data were collected as part of the Silicon-containing secondary organic aerosols in ambient air (Si-SOA) Natural Environment Research Council (NERC) funded project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-27T10:49:46", "updateFrequency": "notPlanned", "dataLineage": "Data produced by University of Birmingham and uploaded to Centre for Environmental Data Anaylsis (CEDA) for archiving.", "removedDataReason": "", "keywords": "SI-SOA, PM2.5, plasma mass spectrometry, ICP-MS", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-18T09:06:12", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2572, "bboxName": "", "eastBoundLongitude": 116.371, "westBoundLongitude": 116.371, "southBoundLatitude": 39.974, "northBoundLatitude": 39.974 }, "verticalExtent": null, "result_field": { "ob_id": 30081, "dataPath": "/badc/deposited2020/si-soa/data/plasma_mass_spectrometry", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12354, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8156, "startTime": "2018-08-07T23:00:00", "endTime": "2019-01-22T00:00:00" }, "resultQuality": { "ob_id": 3366, "explanation": "Data is as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-08" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30028, "uuid": "af61fe4432f64e8a818392d2e8c58b2f", "short_code": "acq", "title": "Si-SOA: inductively coupled plasma mass spectrometry (ICP-MS) PM2.5 measurements taken from IAP-Beijing site", "abstract": "Si-SOA: inductively coupled plasma mass spectrometry (ICP-MS) PM2.5 measurements taken from IAP-Beijing site" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 29996, "uuid": "cb3943b3fd3e41618e2c1283d6b154f6", "short_code": "proj", "title": "Silicon-containing secondary organic aerosols in ambient air (Si-SOA)", "abstract": "The Si-SOA project was lead by the University of Birmingham and ran from April 2018 - June 2019. The project aimed to advance the understanding of an important aspect of air pollution - formation of silicon-organic aerosols via atmospheric oxidation of silicon-containing volatile organic compounds from the personal care products and industries. The project sought to provide definitive evidence on the formation of silicon-containing secondary organic aerosols (Si-SOAs) via photochemical atmospheric processing by using the novel techniques at the Institute of Atmospheric Physics and the University of Birmingham. \r\n\r\nProject outputs will be of interest to research scientists working in the field of atmospheric chemical processing and human health. The results of the project support the study of the oxidation of Si-containing volatile organic compounds (Si-VOCs), which are widely used in personal care products and industrial applications. Si-VOCs are the most abundant VOCs in indoor air and its concentration in Chinese megacities can be over 10 microgram per cubic meter (which is extremely high). Si-VOCs can be oxidised by hydroxyl radicals and form secondary organic aerosols (SOAs), which contributes to the regional haze pollution." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65473, 65484, 65490, 65491, 65503, 65504, 65505, 65506, 65507, 65508, 65509, 65510, 65511, 65512, 65513, 65514, 65515, 65516, 65517, 65518, 65519, 65520, 65521, 65522, 65523 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30041, "uuid": "14f16b7ee7f24f3992e84c22dfb736ca", "short_code": "coll", "title": "PM2.5 analysis results of samples taken from the Institute of Atmospheric Physics -Beijing site by the Si-SOA project", "abstract": "This dataset collection contains the results of the analysis conducted on PM2.5 (particulate matter) samples by the Si-SOA project. The PM2.5 samples were collected in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, China during August 2018 and January 2019 by the Silicon-containing secondary organic aerosols in ambient air (Si-SOA) project. \r\n\r\nThe PM2.5 samples were taken from ambient air at the height of 8m and subjected to a series of analytical techniques, and the data collection is comprised of the following results. \r\n\r\n- The concentration of water-soluble Silicon/water-soluble organic Silicon/water-soluble inorganic Silicon in PM2.5 samples from Ultraviolet-Visible Spectrophotometry\r\n- The concentration of specific ions in PM2.5 samples from Ion Chromatography\r\n- The concentration of water-soluble Silicon in PM2.5 samples from Coupled Plasma - Optical Emission Spectrometry (ICP-OES)\r\n- The concentration of water-soluble elements in PM2.5 samples from Inductively Coupled Plasma Mass Spectrometry (ICP-MS)\r\n- The concentration of elements in PM2.5 samples from X-ray Fluorescence Spectrometry\r\n\r\nThese data support the study of atmospheric processes in relation to fine Silicon-containing particles, which may contribute to the formation of haze and atmospheric pollution." } ], "responsiblepartyinfo_set": [ 129760, 129761, 129762, 129763, 129764, 129766, 129767, 129765, 168413, 129768, 129769, 129770 ], "onlineresource_set": [ 36916, 36917 ] }, { "ob_id": 30030, "uuid": "f73e6dce8284483191c6e832401bb547", "title": "Si-SOA: Concentration of water-soluble Silicon found in PM2.5 samples taken from the Institute of Atmospgeric Physic Beijing site in August 2018 and January 2019", "abstract": "This dataset contains both the concentration of PM2.5 in ambient air sampled at a height of 8 metres and the concentration of water-soluble Silicon found in the PM2.5 samples resulting from the analysis by coupled plasma - optical emission spectrometry (ICP-OES). 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The project aimed to advance the understanding of an important aspect of air pollution - formation of silicon-organic aerosols via atmospheric oxidation of silicon-containing volatile organic compounds from the personal care products and industries. The project sought to provide definitive evidence on the formation of silicon-containing secondary organic aerosols (Si-SOAs) via photochemical atmospheric processing by using the novel techniques at the Institute of Atmospheric Physics and the University of Birmingham. \r\n\r\nProject outputs will be of interest to research scientists working in the field of atmospheric chemical processing and human health. The results of the project support the study of the oxidation of Si-containing volatile organic compounds (Si-VOCs), which are widely used in personal care products and industrial applications. 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Si-VOCs are the most abundant VOCs in indoor air and its concentration in Chinese megacities can be over 10 microgram per cubic meter (which is extremely high). Si-VOCs can be oxidised by hydroxyl radicals and form secondary organic aerosols (SOAs), which contributes to the regional haze pollution." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 65473, 65474, 65475, 65476, 65477, 65478, 65479, 65480, 65481, 65482, 65483, 65484, 65485, 65486, 65487, 65488, 65489, 65490, 65491, 65492, 65493, 65494, 65495, 65496 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30041, "uuid": "14f16b7ee7f24f3992e84c22dfb736ca", "short_code": "coll", "title": "PM2.5 analysis results of samples taken from the Institute of Atmospheric Physics -Beijing site by the Si-SOA project", "abstract": "This dataset collection contains the results of the analysis conducted on PM2.5 (particulate matter) samples by the Si-SOA project. The PM2.5 samples were collected in the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences, China during August 2018 and January 2019 by the Silicon-containing secondary organic aerosols in ambient air (Si-SOA) project. \r\n\r\nThe PM2.5 samples were taken from ambient air at the height of 8m and subjected to a series of analytical techniques, and the data collection is comprised of the following results. \r\n\r\n- The concentration of water-soluble Silicon/water-soluble organic Silicon/water-soluble inorganic Silicon in PM2.5 samples from Ultraviolet-Visible Spectrophotometry\r\n- The concentration of specific ions in PM2.5 samples from Ion Chromatography\r\n- The concentration of water-soluble Silicon in PM2.5 samples from Coupled Plasma - Optical Emission Spectrometry (ICP-OES)\r\n- The concentration of water-soluble elements in PM2.5 samples from Inductively Coupled Plasma Mass Spectrometry (ICP-MS)\r\n- The concentration of elements in PM2.5 samples from X-ray Fluorescence Spectrometry\r\n\r\nThese data support the study of atmospheric processes in relation to fine Silicon-containing particles, which may contribute to the formation of haze and atmospheric pollution." } ], "responsiblepartyinfo_set": [ 129796, 129797, 129793, 129798, 129794, 129795, 129800, 129799, 129801, 168416, 129802, 129803 ], "onlineresource_set": [ 36910, 36911 ] }, { "ob_id": 30042, "uuid": "680ebdcb83c244fdb9d069e2f8952812", "title": "APHH: Direct infusion ultra-high resolution mass spectrometry measurements made at the IAP-Beijing site during the summer and winter campaigns", "abstract": "This dataset contains direct infusion ultra-high resolution mass spectrometry measurements made at the Institute of Atmospheric Physics land station, Beijing (IAP-Beijing) site during the summer and winter APHH-Beijing campaign for the Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) programme.\r\n\r\nDaily quartz microfiber filters were collected with a Tisch high volume sampler. The extracted organic fraction of the collected PM2.5 particles was analysed using an LTQ Orbitrap Velos mass spectrometer with electrospray ionisation to obtain high resolution mass spectra which enable the assignment of molecular formulae to the detected masses.\r\n\r\nFor both seasons, 10 filters were analysed, containing the 5 filters with the highest and lowest mass loadings for each season. The files names are labeled according to the ID of the analysed filter. All dates and times are given in Beijing local time.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-28T09:27:53", "updateFrequency": "notPlanned", "dataLineage": "Data produced by APHH project participants at University of Cambridge and deposited at The Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "APHH, spectrometry, PM2.5", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-03-02T14:25:38", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 1856, "bboxName": "IAP-Beijing", "eastBoundLongitude": 116.371, "westBoundLongitude": 116.371, "southBoundLatitude": 39.974, "northBoundLatitude": 39.974 }, "verticalExtent": null, "result_field": { "ob_id": 30045, "dataPath": "/badc/aphh/data/beijing/cam-orbitrap", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 4897202, "numberOfFiles": 21, "fileFormat": "Data are BADC CSV formatted." }, "timePeriod": { "ob_id": 8162, "startTime": "2016-11-09T09:30:00", "endTime": "2017-06-24T07:28:00" }, "resultQuality": { "ob_id": 3370, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-28" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30044, "uuid": "60a9675fdb2e4b6190b566b773a060c6", "short_code": "acq", "title": "APHH: Direct infusion ultrahigh resolution mass spectrometry measurements made at the IAP-Beijing site during the summer and winter campaigns", "abstract": "APHH: Direct infusion ultrahigh resolution mass spectrometry measurements made at the IAP-Beijing site during the summer and winter campaigns" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 24808, "uuid": "7ed9d8a288814b8b85433b0d3fec0300", "short_code": "proj", "title": "Atmospheric Pollution & Human Health in a Developing Megacity (APHH)", "abstract": "The Atmospheric Pollution & Human Health in a Developing Megacity (APHH) programme has two separate streams of activity looking at urban air pollution and its impact on Health in Chinese and Indian Megacities. The programme is a collaboration between NERC, the Medical Research Council (MRC) in the UK and the National Natural Science Foundation of China (NSFC) in China, and the Ministry of Earth Sciences (MoES) and Department of Biotechnology (DBT) in India." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 24817, "uuid": "648246d2bdc7460b8159a8f9daee7844", "short_code": "coll", "title": "APHH: Atmospheric measurements and model results for the Atmospheric Pollution & Human Health in a Chinese Megacity", "abstract": "The Atmospheric Pollution & Human Health in a Chinese Megacity (APHH) Programme includes several projects making groundbased observations of meteorology, atmospheric chemical species and particulates in and around the city of Beijing. Due to the close working and exchange between the projects and overlap of instruments, this dataset collection contains measurements and related modelling study output produced by all these projects." } ], "responsiblepartyinfo_set": [ 129815, 129816, 129817, 129818, 129820, 129821, 129822, 129823, 129819, 129824 ], "onlineresource_set": [] }, { "ob_id": 30046, "uuid": "d5afa10e50b44229b079c7c5a036e660", "title": "Global Sensitivity Analysis of Tropospheric Ozone and OH: Budgets from three global chemistry-climate models", "abstract": "This dataset contains monthly mean global atmospheric distributions of ozone mixing ratio and methane chemical loss rate (the largest sink for atmospheric methane is the hydroxyl radical, OH) from 105 model runs with three independent global chemistry climate models. The models include the Frontier Research System for Global Change version of the University of California, Irvine Chemical Transport Model (FRSGC/UCI CTM), the Goddard Institute for Space Studies Global Climate Model (GISS GCM) and the Community Atmosphere Model with Chemistry (CAM-Chem). All three models performed the same simulations for a one-year period (broadly representative of 2001 meteorology) under standardised conditions (40 TgN/yr surface NOx emissions, 5 TgN/yr lightning emissions, 500 TgC/yr biogenic isoprene emissions, 1760 ppb tropospheric methane). An ensemble of 105 simulations was performed that included a control run (run 0), a set of runs that were used to build Gaussian Process emulators (runs 1-80), and additional runs that were used to evaluate the emulators (runs 81-104). A spin-up of six months was performed for each run, and monthly mean model results were archived for the following 12 months. The meteorological conditions used were the same in each simulation.\r\n\r\nThe data supports the exploration of the sensitivity of tropospheric ozone and the chemical lifetime of methane in the troposphere (a proxy for the hydroxyl radical, OH) to eight variables: (1) NOx emissions from all surface sources (range: 30-50 TgN/yr), (2) NO emissions from lightning (range: 2-8 TgN/yr), (3) biogenic isoprene emissions (range: 200-800 TgC/yr), (4) dry deposition rates of all deposited species (range: +/- 80%), (5) wet deposition rates of all soluble species (range: +/- 80%), (6) atmospheric humidity as used in the chemistry scheme only (range: +/- 50%), (7) cloud optical depth (range: factor of 10), and (8) turbulent mixing in the planetary boundary layer (range: factor of 100). The design of the ensemble runs used a Latin Hypercube method to sample this eight-dimensional parameter space to achieve optimal coverage with only 80 simulations. A separate design was used to select an additional 24 simulations to evaluate the emulators built from the standard 80 runs. The specifications for each simulation (total annual emission rate for surface NOx, biogenic isoprene and lightning NO, and the scaling factors applied to native model dry and wet deposition rates, humidity, cloud optical depth, and boundary layer diffusion coefficient) are provided with the dataset.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-13T16:45:39", "updateFrequency": "notPlanned", "dataLineage": "Original global model results generated for the NERC project \"Quantifying and Reducing Uncertainty in the Processes Controlling Tropospheric Ozone and OH\". Selected variables provided in NetCDF format by model investigators and harmonized in CF-compliant NetCDF for submission to CEDA.", "removedDataReason": "", "keywords": "Tropospheric ozone burden, tropospheric methane lifetime, Gaussian process emulation, sensitivity analysis", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-04-24T09:55:52", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2579, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30047, "dataPath": "/badc/deposited2020/QRU-TropO3OH/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12018962087, "numberOfFiles": 4, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8163, "startTime": "2001-01-01T00:00:00", "endTime": "2001-12-31T00:00:00" }, "resultQuality": { "ob_id": 3371, "explanation": "The data were quality controlled by the data providers prior to being made available to CEDA. The data are well formed CF-compliant NetCDF.", "passesTest": true, "resultTitle": "Quantifying and Reducing Uncertainty in the Processes Controlling Tropospheric Ozone and OH Data Quality Statement", "date": "2020-01-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 30084, "uuid": "3ecd03008b6542c3a141125b9dfed789", "short_code": "cmppr", "title": "Global Sensitivity Analysis of Tropospheric Ozone and OH: Budgets from three global chemistry-climate models", "abstract": "Global Sensitivity Analysis of Tropospheric Ozone and OH: Budgets from three global chemistry-climate models" }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30048, "uuid": "c08a430f28964283824ba0889ed4cc2c", "short_code": "proj", "title": "Quantifying and Reducing Uncertainty in the Processes Controlling Tropospheric Ozone and OH", "abstract": "Understanding the behaviour of hydroxyl (OH) radicals in the troposphere is vital for explaining and predicting atmospheric composition change and its impacts on air quality and climate. The observed atmospheric abundance of ozone and methane has increased substantially over the past century due to human activity, and the fates of these gases are strongly coupled through the short-lived OH radical. However, we do not currently understand the relative importance of the different processes and variables that govern the abundance of these gases. State-of-the-art global chemistry-climate models show differences in methane lifetime of almost a factor of two, preventing them from simulating realistically the observed atmospheric build-up of methane or correctly attributing its causes. These models are also unable to reproduce ozone observations from the late 19th century, or more recent ozone trends observed over the past two decades.\r\n\r\nThis project addressed these weaknesses by using novel statistical approaches to quantify the sensitivity of OH, O3 and CH4 in global models to the processes and inputs that govern them, and by developing new observational constraints to reduce this uncertainty. We applied tried and tested emulation methods to reproduce the response of computationally-expensive atmospheric models and to permit a more complete and quantitative assessment of process contributions to uncertainty in trace gas abundance. A unique aspect of this project is that we have applied these approaches to a number of independent global models to provide a robust assessment of model responses and to identify the causes of model differences for the first time.\r\n\r\nThe overarching aim of the project was to provide fresh scientific insight into the chemical and dynamical processes governing tropospheric OH and the related gases ozone and methane. This allowed us to quantify the importance of different processes, explain the diversity in model assessments of past and future atmospheric composition change, and attribute observed changes to specific drivers.\r\n\r\nOur principal scientific objectives were:\r\n\r\n1. To identify the main causes of uncertainty in modelled tropospheric OH, O3, and CH4, allowing us to quantify for the first time how our understanding of different processes and variables contributes to variation in atmospheric composition, and to explain the large differences in model responses seen in assessments of past and future atmospheric composition\r\n\r\n2. To use atmospheric composition observations to place formal statistical constraints on model uncertainty, allowing us to determine which processes can be efficiently tested with observations and permitting design of more critical, process based approaches to model evaluation\r\n\r\n3. To apply these novel approaches and constraints to provide a new and more robust attribution of the causes of past and future O3 and CH4 changes, and permit formal quantification of the associated uncertainty.\r\n\r\nDuration of project: Jan 2016 - Dec 2018\r\nNERC Reference: NE/N003411/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50416, 50420, 50559, 50561, 54088, 54093, 54864, 54865, 54866, 54867, 54868, 54869, 54870, 91550 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 129829, 129830, 129831, 129832, 129833, 129834, 129836, 129835, 129837, 130373, 129838 ], "onlineresource_set": [ 36831, 36937 ] }, { "ob_id": 30050, "uuid": "77d63a69e3554412a40c9a9ba564e3f9", "title": "SWIGS: Gorgon Magnetohydrodyamic Code Simulation Data: Magnetospheric and Ionospheric Conditions under Southward IMF for 0-90 degree Dipole Tilts", "abstract": "This dataset contains data outputs generated using the Gorgon Magnetohydrodynamic (MHD) code, for simulations of the steady-state magnetosphere-ionosphere system during southward interplanetary magnetic field (IMF) with dipole tilt angles from 0-90 degrees. This data were collected as part of the NERC project Space Weather Impacts on Ground-based Systems (SWIGS).\r\n\r\nThe MHD equations were solved in the magnetosphere on a regular 3-D cartesian grid of resolution 0.5 RE (Earth radii), covering a domain of dimensions (-30,90) RE in X, (-40,40) RE in Y and (-40,40) RE in Z with an inner boundary at 4 RE. In this coordinate system the Sun lies in the negative X-direction, the Z axis is aligned to the dipole in the 0 degree tilt case (where positive tilt points the north magnetic pole towards the Sun), and Y completes the right-handed set. The ionospheric variables were calculated on a separate 2-D spherical grid of dimensions 66x128 in latitude and longitude (with the north pole at 90 degrees latitude and the sun at 180 degrees longitude), coupled to the magnetospheric domain at the inner boundary. \r\n\r\nOutput data is timestamped in seconds and is defined at the centre of the grid cells. The simulation data corresponding to each dipole tilt are stored in separate directories 'XXdeg', e.g. in '00deg' for a 0 degree tilt angle. The data are stored in hdf5 format.\r\n\r\nThe magnetospheric variables are stored in the files 'Gorgon_[YYYYMMDD]_[XX]deg_MS_params_[XXXXX]s.hdf5' where XX is the tilt angle in degrees and XXXXX is the simulation time in seconds. The magnetospheric data includes the magnetic field ('Bvec_c'), plasma bulk velocity ('vvec') and electric current density ('jvec') after 4h of simulation, as well as the magnetic field and velocity in 5 minute intervals for the preceding 30 minutes. The dataset for each magnetospheric variable is of shape (240,160,160,3) where the first 3 dimensions are the grid indices in (X,Y,Z) indexed from negative to positive, and the final dimension is the cartesian vector components in (i,j,k). \r\n\r\nSimilarly, the ionospheric data are stored as 'Gorgon_[YYYYMMDD]_[XX]deg_IS_params_[XXXXX]s.hdf5', containing the field-aligned current ('FAC') and electric potential ('phi') after 4h of simulation, as well as the potential in 5 minute intervals for the preceding 30 minutes. The dataset for each ionospheric variable is of shape (66, 128) where the first dimension is the grid index in colatitude, indexed from the north towards the south (i.e. 0 to 180 degrees), and the second dimension is the grid index in longitude, indexed from midnight towards noon via dawn (i.e. 0 to 360 degrees).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-01-29T13:54:55", "updateFrequency": "notPlanned", "dataLineage": "Data collected by the project participants and sent to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "SWIGS, Space, weather, Gorgon Magnetohydrodyamic Code", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-26T14:16:38", "doiPublishedTime": "2020-04-09T13:23:57", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30051, "dataPath": "/badc/deposited2020/swigs/data/gorgon-dip-tilt/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 22124097315, "numberOfFiles": 142, "fileFormat": "Data are HDF5 formatted." }, "timePeriod": { "ob_id": 10269, "startTime": "2020-01-29T00:00:00", "endTime": "2020-01-29T00:00:00" }, "resultQuality": { "ob_id": 3373, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-01-29" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30052, "uuid": "e19b85241a2c4397be84efa925b8b500", "short_code": "comp", "title": "Gorgon Magnetohydrodynamic Code for Planetary Magnetospheres", "abstract": "A global magnetosphere code which solves the semi-conservative resistive MHD equations on a uniform, staggered 3-D cartesian grid. Coupling with the ionosphere is achieved using a thin-shell ionosphere model." }, "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": 30053, "uuid": "d085ca3d2bef4d1a8ba05fa999c86074", "short_code": "proj", "title": "Space Weather Impacts on Ground-based Systems (SWIGS)", "abstract": "Space weather describes the changing properties of near-Earth space, which influences the flow of electrical currents in this region, particularly within the ionosphere and magnetosphere. Space weather results from solar magnetic activity, which waxes and wanes over the Sunspot cycle of 11 years, due to eruptions of electrically charged material from the Sun''s outer atmosphere. Particularly severe space weather can affect ground-based, electrically conducting infrastructures such as power transmission systems (National Grid), pipelines and railways. Ground based networks are at risk because rapidly changing electrical currents in space, driven by space weather, cause rapid geomagnetic field changes on the ground.\r\nThese magnetic changes give rise to electric fields in the Earth that act as a ''battery'' across conducting infrastructures. This ''battery'' causes geomagnetically induced currents (GIC) to flow to or from the Earth, through conducting networks, instead of in the more resistive ground. These GIC upset the safe operation of transformers, risking damage and blackouts. GIC also cause enhanced corrosion\r\n in long metal pipeline networks and interfere with railway signalling systems. \r\n\r\nSevere space weather in March 1989 damaged power transformers in the UK and caused a long blackout across Quebec, Canada. The most extreme space weather event known - the ''Carrington Event'' of 1859 - caused widespread failures and instabilities in telegraph networks, fires in telegraph offices and auroral displays to low latitudes. The likelihood of another such extreme event is estimated to be around 10% per decade. Severe space weather is therefore recognised in the UK government''s\r\nNational Risk Register as a one-in-two to one-in-twenty year event, for which industry and government needs to plan to mitigate the risk. Some studies have estimated the economic consequence of space weather and GIC to run to billions of dollars per day in the major advanced economies, through the prolonged loss of electrical power.\r\n\r\nThere are mathematical models of how GIC are caused by space weather and where in the UK National Grid they may appear (there are no models of GIC flow in UK pipelines or railway networks). However these models are quite limited in what they can do and may therefore not provide a true picture of GIC risk in grounded systems, for example highlighting some locations as being at risk, when in fact\r\nany problems lie elsewhere. The electrical model that has been developed to represent GIC at transformer substations in the National Grid misses key features, such as a model of the 132kV transmission system of England and Wales, or any model for Northern Ireland. The conductivity of the subsurface of the UK is known only partly and in some areas not at all well. (We need to know the conductivity in order to compute the electric field that acts as the ''battery'' for GIC.) The\r\nUK GIC models only ''now-cast'', at best, and they have no forecast capability, even though this is a stated need of industry and government. We do not have tried and tested now-cast models, or even forecast models, of magnetic variations on the ground. This is because of our under-developed understanding of how currents flow in the ionosphere and magnetosphere, how these interconnect and how they relate to conditions in the solar wind.\r\n\r\nIn this project we will upgrade existing or create new models that relate geomagnetically induced currents GIC in power, pipe and railway networks to ionospheric, magnetospheric and solar wind conditions. These models will address the issues we have identified with the current generation of models and their capabilities and provide accurate data for industry and governments to assess our risk from space weather. In making progress on these issues we will also radically improve on our physical understanding of the way electrical currents and electromagnetic fields interact near and in the Earth and how they affect the important technologies we rely on." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [ 10709 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 129842, 129847, 129848, 129849, 129851, 129852, 129853, 129850, 129843, 168900, 129844, 129845, 129846 ], "onlineresource_set": [] }, { "ob_id": 30054, "uuid": "18ef8247f52a4cb6a14013f8235cc1eb", "title": "University of Leicester GOSAT Proxy XCH4 v9.0", "abstract": "The University of Leicester GOSAT Proxy XCH4 v9.0 data set contains column-averaged dry-air mole fraction of methane (XCH4) generated from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using the University of Leicester Full-Physics retrieval scheme (UoL-FP) using the Proxy retrieval approach.\r\n\r\nThis data is an NCEO funded update/extension to the European Space Agency Climate Change Initiative (CCI) CH4_GOS_OCPR V7.0. and the Copernicus Climate Change Service (C3S) CH_4 v7.2 data sets. It's a full reprocessing, based on different underlying L1B radiance data with additional changes. The latest version of the GOSAT Level 1B files (version 210.210) was acquired directly from the National Institute for Environmental Studies (NIES) GOSAT Data Archive Service (GDAS) Data Server and are processed with the Leicester Retrieval Preparation Toolset to extract the measured radiances along with all required sounding-specific ancillary information such as the measurement time, location and geometry. These measured radiances have the recommended radiometric calibration and degradation corrections applied as per Yoshida et al., 2013 with an estimate of the spectral noise derived from the standard deviation of the out-of-band signal. The spectral data were then inputted into the UoL-FP retrieval algorithm where the Proxy retrieval approach is used to obtain the column-averaged dry-air mole fraction of methane (XCH4). Post-filtering and bias correction against the Total Carbon Column Observing Network is then performed. See process information and documentation for further details.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2022-06-15T16:21:34", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the University of Leicester project team and delivered to Centre for Environmental Data Analysis (CEDA) for archival and publication. The data was produce under NCEO grant nceo020005", "removedDataReason": "", "keywords": "GOSAT, CH4, Methane", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-05-07T16:04:50", "doiPublishedTime": "2020-05-07T16:16:37", "removedDataTime": null, "geographicExtent": { "ob_id": 2620, "bboxName": "UOL XCH4 Proxy v9", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30281, "dataPath": "/neodc/gosat/data/ch4/nceov1.0/CH4_GOS_OCPR/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 19479912781, "numberOfFiles": 4530, "fileFormat": "CF compliant NetCDF" }, "timePeriod": { "ob_id": 8244, "startTime": "2009-04-23T00:00:00", "endTime": "2021-12-31T23:59:59" }, "resultQuality": { "ob_id": 3411, "explanation": "The data has been fully validated by the University of Leicester project team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-05-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 30282, "uuid": "12326315f26346569bc432d4d9ad9bc7", "short_code": "cmppr", "title": "University of Leicester GOSAT Proxy XCH4 v9.0 Composite Process", "abstract": "The latest version of the GOSAT Level 1B files (version 210.210) are acquired directly from the NIES GDAS Data Server and are processed with the Leicester Retrieval Preparation Toolset to extract the measured radiances along with all required sounding-specific ancillary information such as the measurement time, location and geometry. These measured radiances have the recommended radiometric calibration and degradation corrections applied as per Yoshida et al., 2013 with an estimate of the spectral noise derived from the standard deviation of the out-of-band signal. The spectral data were then inputted into the UoL-FP retrieval algorithm where the Proxy retrieval approach is used to obtain the column-averaged dry-air mole fraction of methane (XCH4). Post-filtering and bias correction against the Total Carbon Column Observing Network is then performed" }, "imageDetails": [ 130 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 5002, "uuid": "60e718d3f2957f742c89b2b4fc159718", "short_code": "proj", "title": "National Centre for Earth Observation (NCEO)", "abstract": "The National Centre for Earth Observation is a partnership of scientists and institutions, from a range of disciplines, who are using data from Earth observation satellites to monitor global and regional changes in the environment and to improve understanding of the Earth system so that we can predict future environmental conditions.\r\n\r\nNCEO's Vision is to unlock the full potential of Earth observation to monitor, diagnose and predict climate and environmental changes, ensuring that these scientific advances are delivered to the wider community embedded in world class science." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50542, 50543, 55535, 63576, 66452, 66456, 66719, 68622, 68623, 68624, 68625, 68626, 68627, 68628, 68629, 68630, 68631, 68632, 68633, 68634, 68635, 68636, 68637, 68638, 68639, 68640, 68641, 68642, 68643, 68644, 68645, 68646, 69629 ], "vocabularyKeywords": [], "identifier_set": [ 10713 ], "observationcollection_set": [ { "ob_id": 30127, "uuid": "82b29f96b8c94db28ecc51a479f8c9c6", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Core datasets", "abstract": "This NCEO Core data set collection contains data generated by the National Centre for Earth Observation core scientific programmes. NCEO is a National Environment Research Council (NERC) research centre with more than 80 scientists distributed across leading UK universities and research organisations and led by Professor John Remedios at the University of Leicester.\r\n\r\nNCEO provides the UK with core expertise in Earth Observation science, data sets and merging techniques, and model evaluation to underpin Earth System research and the UK’s international contribution to environmental science. NCEO scientists work strategically with space agencies, play significant roles in mission planning, and generate internationally-recognised data products from 20 different satellite instruments." } ], "responsiblepartyinfo_set": [ 130872, 130863, 130874, 130867, 130866, 130865, 130868, 130871, 130870, 130869, 130873, 130864 ], "onlineresource_set": [ 37197, 37198, 37199, 37200, 37201, 87360, 87361, 87362, 87363, 87364, 87365, 87366, 87367, 87368, 87369, 87370, 87371, 87372, 89230, 89231, 89232, 89233, 89234, 89235, 89236, 89237, 89238, 89239, 89240, 89241, 89242, 89243, 89244, 89245, 89246, 89247, 89248, 88110 ] }, { "ob_id": 30055, "uuid": "326bf808aedd41fd85594fc06678d20a", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): ATSR2-AATSR monthly gridded cloud properties, version 3.0", "abstract": "The Cloud_cci ATSR2-AATSRv3 dataset (covering 1995-2012) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on measurements from the ATSR2 and AATSR instruments (onboard the ERS2 and ENVISAT satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci ATSR2-AATSRv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. \r\n\r\nThe data in this dataset are a subset of the ATSR2-AATSR L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003. \r\nTo cite the full dataset, please use the following citation: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-04-30T23:13:30", "updateFrequency": "notPlanned", "dataLineage": "The data here form a subset of the ATSR2-AATSR v3.0 products produced by the ESA Cloud CCI, a copy of which has been supplied to the Centre for Environmental Data Analysis in the context of the ESA CCI Open Data Portal Project. The full dataset is collectively referenced by the following DOI: Poulsen, Caroline; McGarragh, Greg; Thomas, Gareth; Stengel, Martin; Christensen, Matthew; Povey, Adam; Proud, Simon; Carboni, Elisa; Hollmann, Rainer; Grainger, Don (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci ATSR2-AATSR L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD) and Rutherford Appleton Laboratory (Dataset Producer), DOI:10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003.", "removedDataReason": "", "keywords": "ESA, CCI, Clouds", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.5x0.5 degree", "status": "completed", "dataPublishedTime": "2020-03-26T09:39:39", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30056, "dataPath": "/neodc/esacci/cloud/data/version3/L3C/ATSR2-AATSR/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 34754502193, "numberOfFiles": 198, "fileFormat": "Data are netCDF formatted" }, "timePeriod": { "ob_id": 8164, "startTime": "1995-06-01T00:00:00", "endTime": "2012-04-30T23:59:59" }, "resultQuality": { "ob_id": 3375, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-04" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 10920, "uuid": "d4ffd8b1c9ac41439e90bdb985a24e07", "short_code": "acq", "title": "Acquisition Process for: Data from ENVISAT AATSR at Envisat for the ENVISAT Campaign", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: ENVISAT AATSR; 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These global datasets contain cloud fraction, cloud top level estimates (pressure, height, and temperature), cloud thermodynamic phase, spectral cloud albedo, cloud effective radius, cloud optical thickness as well as cloud liquid and ice water content.\r\n\r\nThe AATSR-MODIS-AVHRR heritage product family obtained by CC4CL is based on measurements from ATSR-2/ERS-2, AATSR/ENVISAT, MODIS/AQUA, MODIS/TERRA, and AVHRR on-board NOAA-7, 9, 11, 12, 14, 15,16, 17, 18,19, and MetOp-A. The second product family contains cloud properties derived from ENVISAT’s AATSR and MERIS observations using the synergetic retrieval system FAME-C.\r\n\r\nIn the first phase (2010 – 2013) of the Cloud_cci project prototype retrieval versions have been established leading to preliminary results covering 2007, 2008, and 2009, herein referred to as demonstrator datasets. In Phase 2 (2014 – 2016) both retrieval schemes have been substantially improved enhancing the data quality of the cloud products spanning the time period from Jan 1st 1982 to Dec 31st 2014.\r\n\r\nConsiderations for climate applications:\r\nDue to the short period (i.e. 3 years) of the current available demonstrator datasets, it is not possible to perform long-term data comparisons or to support long-term climate analysis.\r\n\r\nPlease be aware of the fact that by the end of 2016 at the latest these prototype datasets will be replaced by the complete multi-decadal Cloud_cci climatology (1982 – 2014) together with updated Product User Guide (PUG) and Product Validation and Intercomparison Report (PVIR) documents. \r\n\r\nWe would like to stress that one of the main objectives in the second phase of the Cloud_cci project has been the further development and improvement of both retrieval schemes and their processing systems. As a consequence, the quality and accuracy of the final cloud products have been considerably improved compared to the currently available demonstrator datasets." }, { "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": [ 129869, 129871, 129872, 129874, 129875, 129876, 129873, 130018, 130019, 130020, 130021, 130022, 130023, 130024, 130025, 130026, 130027 ], "onlineresource_set": [ 36832, 36833, 36898, 36945, 36946, 36950, 36951, 36952, 36953, 36947, 36948, 36949 ] }, { "ob_id": 30059, "uuid": "fb3750f5b2544403873f8788b3ed7817", "title": "ESA Cloud Climate Change Initiative (Cloud CCI): AVHRR-AM monthly gridded cloud properties, version 3.0", "abstract": "The Cloud_cci AVHRR-AMv3 dataset (covering 1991-2016) was generated within the Cloud_cci project which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements. \r\n\r\nThis dataset is based on AVHRR (onboard NOAA-12, NOAA-15, NOAA-17, Metop-A) measurements and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-AMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. \r\n\r\nThe data in this dataset are a subset of the AVHRR-AM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/doi:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-04T14:59:32", "updateFrequency": "notPlanned", "dataLineage": "The data here form a subset of the AVHRR-AM v3.0 products produced by the ESA Cloud CCI, a copy of which has been supplied to the Centre for Environmental Data Analysis in the context of the ESA CCI Open Data Portal Project. The full dataset is collectively referenced by the following DOI: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-AM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V003.", "removedDataReason": "", "keywords": "ESA, CCI", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.5x0.5 degree", "status": "completed", "dataPublishedTime": "2020-03-26T09:43:03", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30060, "dataPath": "/neodc/esacci/cloud/data/version3/L3C/AVHRR-AM/v3.0/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 47863738017, "numberOfFiles": 305, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8220, "startTime": "1991-09-01T00:00:00", "endTime": "2016-12-31T23:00:00" }, "resultQuality": { "ob_id": 3376, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-04" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 8453, "uuid": "bb890e4dd33645689c6fc769d9c1dc8f", "short_code": "acq", "title": "Acquisition Process for: Data from AVHRR series at NOAA polar orbiting satellites for the NOAA Polar Orbiting Satellite Series Project", "abstract": "This acquisition is comprised of the following: INSTRUMENTS: AVHRR series; 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These global datasets contain cloud fraction, cloud top level estimates (pressure, height, and temperature), cloud thermodynamic phase, spectral cloud albedo, cloud effective radius, cloud optical thickness as well as cloud liquid and ice water content.\r\n\r\nThe AATSR-MODIS-AVHRR heritage product family obtained by CC4CL is based on measurements from ATSR-2/ERS-2, AATSR/ENVISAT, MODIS/AQUA, MODIS/TERRA, and AVHRR on-board NOAA-7, 9, 11, 12, 14, 15,16, 17, 18,19, and MetOp-A. The second product family contains cloud properties derived from ENVISAT’s AATSR and MERIS observations using the synergetic retrieval system FAME-C.\r\n\r\nIn the first phase (2010 – 2013) of the Cloud_cci project prototype retrieval versions have been established leading to preliminary results covering 2007, 2008, and 2009, herein referred to as demonstrator datasets. In Phase 2 (2014 – 2016) both retrieval schemes have been substantially improved enhancing the data quality of the cloud products spanning the time period from Jan 1st 1982 to Dec 31st 2014.\r\n\r\nConsiderations for climate applications:\r\nDue to the short period (i.e. 3 years) of the current available demonstrator datasets, it is not possible to perform long-term data comparisons or to support long-term climate analysis.\r\n\r\nPlease be aware of the fact that by the end of 2016 at the latest these prototype datasets will be replaced by the complete multi-decadal Cloud_cci climatology (1982 – 2014) together with updated Product User Guide (PUG) and Product Validation and Intercomparison Report (PVIR) documents. \r\n\r\nWe would like to stress that one of the main objectives in the second phase of the Cloud_cci project has been the further development and improvement of both retrieval schemes and their processing systems. As a consequence, the quality and accuracy of the final cloud products have been considerably improved compared to the currently available demonstrator datasets." } ], "responsiblepartyinfo_set": [ 129892, 129885, 129887, 129888, 129890, 129891, 130028, 129889, 130029, 130030, 130031, 130032, 130033, 130034, 130035 ], "onlineresource_set": [ 36843, 36844, 36899, 36954, 36955, 36956, 36957, 36962, 36958, 36959, 36960, 36961 ] }, { "ob_id": 30067, "uuid": "004fd44ff5124174ad3c03dd2c67d548", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): AVHRR-PM monthly gridded cloud properties, version 3.0", "abstract": "The Cloud_cci AVHRR-PMv3 dataset (covering 1982-2016) was generated within the Cloud_cci project, which was funded by the European Space Agency (ESA) as part of the ESA Climate Change Initiative (CCI) programme (Contract No.: 4000109870/13/I-NB). This dataset is one of the 6 datasets generated in Cloud_cci; all of them being based on passive-imager satellite measurements.\r\n\r\nThis dataset is based on measurements from AVHRR (onboard the NOAA-7, NOAA-9, NOAA-11, NOAA-14, NOAA-16, NOAA-18, NOAA-19 satellites) and contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL; Sus et al., 2018; McGarragh et al., 2018) retrieval framework. The core cloud properties contained in the Cloud_cci AVHRR-PMv3 dataset are cloud mask/fraction, cloud phase, cloud top pressure/height/temperature, cloud optical thickness, cloud effective radius and cloud liquid/ice water path. Spectral cloud albedo is also included as experimental product. The cloud properties are available at different processing levels: This particular dataset contains Level-3C (monthly averages and histograms) data, while Level-3U (globally gridded, unaveraged data fields) is also available as a separate dataset. Pixel-based uncertainty estimates come along with all properties and have been propagated into the Level-3C data. \r\n\r\nThe data in this dataset are a subset of the AVHRR-PM L3C / L3U cloud products version 3.0 dataset produced by the ESA Cloud_cci project available from https://dx.doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003. To cite the full dataset, please use the following citation: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T13:13:08", "updateFrequency": "notPlanned", "dataLineage": "The data here form a subset of the AVHRR-PM v3.0 products produced by the ESA Cloud CCI, a copy of which has been supplied to the Centre for Environmental Data Analysis in the context of the ESA CCI Open Data Portal Project. The full dataset is collectively referenced by the following DOI: Stengel, Martin; Sus, Oliver; Stapelberg, Stefan; Finkensieper, Stephan; Würzler, Benjamin; Philipp, Daniel; Hollmann, Rainer; Poulsen, Caroline (2019): ESA Cloud Climate Change Initiative (ESA Cloud_cci) data: Cloud_cci AVHRR-PM L3C/L3U CLD_PRODUCTS v3.0, Deutscher Wetterdienst (DWD), DOI:10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V003.", "removedDataReason": "", "keywords": "ESA, CCI", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.5x0.5 degree", "status": "completed", "dataPublishedTime": "2020-03-26T09:45:34", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30068, "dataPath": 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"title": "ESA Cloud Climate Change Initiative (CCI) Dataset Collection", "abstract": "This dataset collection contains cloud products produced by the Cloud project within the ESA Climate Change Initiative (CCI). \r\n\r\nThe ultimate objective of the ESA Cloud Climate Change Initiative (Cloud_cci) project is to provide long-term coherent cloud property datasets exploiting the synergic capabilities of different Earth observation missions allowing for improved accuracies and enhanced temporal and spatial sampling better than those provided by the single sources.\r\n\r\nCC4CL (Community Cloud Retrieval for Climate) and FAME-C (Freie Universität Berlin AATSR MERIS Cloud) are optimal estimation based retrieval systems providing GCOS cloud property Essential Climate Variables (ECVs) including uncertainty estimates. These global datasets contain cloud fraction, cloud top level estimates (pressure, height, and temperature), cloud thermodynamic phase, spectral cloud albedo, cloud effective radius, cloud optical thickness as well as cloud liquid and ice water content.\r\n\r\nThe AATSR-MODIS-AVHRR heritage product family obtained by CC4CL is based on measurements from ATSR-2/ERS-2, AATSR/ENVISAT, MODIS/AQUA, MODIS/TERRA, and AVHRR on-board NOAA-7, 9, 11, 12, 14, 15,16, 17, 18,19, and MetOp-A. The second product family contains cloud properties derived from ENVISAT’s AATSR and MERIS observations using the synergetic retrieval system FAME-C.\r\n\r\nIn the first phase (2010 – 2013) of the Cloud_cci project prototype retrieval versions have been established leading to preliminary results covering 2007, 2008, and 2009, herein referred to as demonstrator datasets. In Phase 2 (2014 – 2016) both retrieval schemes have been substantially improved enhancing the data quality of the cloud products spanning the time period from Jan 1st 1982 to Dec 31st 2014.\r\n\r\nConsiderations for climate applications:\r\nDue to the short period (i.e. 3 years) of the current available demonstrator datasets, it is not possible to perform long-term data comparisons or to support long-term climate analysis.\r\n\r\nPlease be aware of the fact that by the end of 2016 at the latest these prototype datasets will be replaced by the complete multi-decadal Cloud_cci climatology (1982 – 2014) together with updated Product User Guide (PUG) and Product Validation and Intercomparison Report (PVIR) documents. \r\n\r\nWe would like to stress that one of the main objectives in the second phase of the Cloud_cci project has been the further development and improvement of both retrieval schemes and their processing systems. As a consequence, the quality and accuracy of the final cloud products have been considerably improved compared to the currently available demonstrator datasets." } ], "responsiblepartyinfo_set": [ 129921, 129922, 129917, 129918, 129919, 129924, 130036, 129920, 130037, 130038, 130039, 130040, 130041, 130042, 130043 ], "onlineresource_set": [ 36869, 36900, 36971, 36969, 36970, 36963, 36964, 36965, 36966, 36967, 36968, 36868 ] }, { "ob_id": 30085, "uuid": "3f2f92ad04f84e65a2399deeafd8bbbc", "title": "INTEGRATE: climate model simulations data to study the Asian climate response to Atlantic Multidecadal Oscillation (AMO) using the Intermediate General Circulation Model version 4 (IGCM4)", "abstract": "This dataset contains model simulated data to study the response of Asian summer climate to AMO-like (Atlantic Multidecadal Oscillation) sea surface temperature (SST) anomalies using the Intermediate General Circulation Model version 4 (IGCM4; Joshi et al., 2015). This dataset also contains the AMO SST patterns from seven Coupled Model Intercomparison Project (CMIP5)/Paleoclimate Modelling Intercomparison Project Phase (PMIP3) global climate models (Ratna et al., 2019), which are used to explore the sensitivity of the atmospheric response to the SST pattern.\r\n\r\nThese data were collected as part of the Natural Environment Research Council (NERC) funded 'An integrated data-model study of interactions between tropical monsoons and extra-tropical climate variability and extremes (INTEGRATE)' project (2016-2020) at the Climatic Research Unit, University of East Anglia. The project studied the interactions between tropics and extratropical climate variability.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-05T09:25:26", "updateFrequency": "notPlanned", "dataLineage": "This data were generated using the Intermediate General Circulation Model version 4 (IGCM4; Joshi et al., 2015). The model is integrated for 55 years (idealised years) and monthly mean output are generated for the analysis and saved as NetCDF format using Climate Data Operator (CDO). The data were then sent to the Centre for Environmental Data Anaylsis (CEDA) for archiving.", "removedDataReason": "", "keywords": "INTEGRATE, AMO, model, IGCM4", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-25T10:16:43", "doiPublishedTime": "2020-06-17T14:17:56", "removedDataTime": null, "geographicExtent": { "ob_id": 2581, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30086, "dataPath": "/badc/deposited2020/integrate/data/v1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 14925928761, "numberOfFiles": 706, "fileFormat": "Data are netCDF formatted." }, "timePeriod": null, "resultQuality": { "ob_id": 3379, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-05" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30088, "uuid": "3d2ed346f82d4532a4b792a5d6b61b7d", "short_code": "comp", "title": "Intermediate General Circulation Model version 4 (IGCM4) is deployed on the High Performance Compute Cluster at the University of East Anglia, Norwich, U.K.", "abstract": "The model experiments for this study are conducted using the Intermediate General Circulation Model version 4 (IGCM4) on the High Performance Compute Cluster at the University of East Anglia, Norwich, U.K." }, "procedureCompositeProcess": null, "imageDetails": [ 2 ], "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": 30087, "uuid": "3c6c92b8eda5411b88d11445481fb6f8", "short_code": "proj", "title": "An integrated data-model study of interactions between tropical monsoons and extra-tropical climate variability and extremes (INTEGRATE)", "abstract": "In order to predict the evolution of inter-regional linkages this century, it is crucial to understand how they have evolved in the past. This is particularly important because extremes such as drought are modulated by decadal variability. The project was motivated by recent identification of multidecadal links between 2000-year reconstructions of Tibetan Plateau precipitation and Northern Hemisphere temperature. The project went far beyond this initial finding by combining insights gained from coupled climate models and observations (instrumental, tree-ring and documentary records) to identify large-scale modes that link the variability of tropical monsoons and northern extratropical climates on multiple timescales. The project aimed to (i) better understand the teleconnections between monsoon and extratropical regions (with particular emphasis on the Arctic), (ii) evaluate the ability of climate models to reproduce observed behaviour including regional extremes, (iii) explore mechanisms that drive the observed behaviour and understand how the linkages may evolve under future climate change using simulations with a hierarchy of models. Paleoclimate records, including temperature- sensitive tree-ring records from the Eurasian Arctic and precipitation-sensitive series from the Asian monsoon regions provide a unique opportunity to identify associations on annual to multidecadal timescales. CMIP5 and CMIP6 \"control\" and \"last millennium\" runs were used to assess simulated unforced variability and response to forcings such as volcanoes and to consider SST and atmospheric circulation patterns associated with periods of extremes in the monsoon, extratropical and Arctic regions. New simulations with imposed anomalies (e.g. sea or land surface temperatures in particular regions) in conjunction with external forcing were designed to explore the roles of basin-wide changes or land-surface interactions in generating and synchronising decadal variability. Implications for future climate change were considered using model simulations and our improved understanding of mechanisms." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6021, 6022, 28417, 50559, 50561, 53109, 60438, 70655, 70656, 70657, 70658, 70659, 70660, 70661, 70662, 70663, 70664 ], "vocabularyKeywords": [], "identifier_set": [ 10724 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 129982, 129983, 129984, 129985, 129986, 129987, 129989, 129988, 168903, 129990, 129991 ], "onlineresource_set": [ 94858, 94859, 94860 ] }, { "ob_id": 30093, "uuid": "0c39809848ce47bb850d8ca2045e40f2", "title": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI): Isoprene concentration measurements at Wytham Woods (UK) during the summer of 2018", "abstract": "This dataset contains the time series of isoprene concentrations measured at Wytham Woods in the summer of 2018 during the Wytham Isoprene iDirac Oak Tree Measurements (WIsDOM) campaign, part of the NERC funded Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI) project.\r\n\r\nIsoprene is the largest biogenic emission on the planet and impacts air composition and climate. Seasonal data is important to underpin the biosphere-atmosphere exchange. A portable gas chromatograph, the iDirac (Bolas et al., Atmos. Meas. Tech, 2020) was used to monitor isoprene for a whole growing season (May-end September) in 2018 at Wytham Woods, an ancient woodland in the UK. Measurements were collected at 4 heights across the forest canopy: at ground level (0.53 m, inlet 1), in the under storey (7.25 m, inlet 2), at the crown level (13.17 m, inlet 3) and above the canopy (15.55 m, inlet 4). \r\n\r\nAncillary data (air temperature and photosynthetic active radiation, or PAR) were also collected.\r\n\r\nThe WIsDOM campaign was a joint effort of Cranfield University and the University of Cambridge.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-05T14:35:22", "updateFrequency": "notPlanned", "dataLineage": "Data were collected by the project team and delivered to the Centre for Environmental data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "BALI, WIsDOM, isoprene", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-11T12:30:12", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2583, "bboxName": "Wytham woods", "eastBoundLongitude": -1.338389, "westBoundLongitude": -1.338389, "southBoundLatitude": 51.773389, "northBoundLatitude": 51.773389 }, "verticalExtent": null, "result_field": { "ob_id": 30094, "dataPath": "/badc/deposited2018/bali/data/wytham/isoprene/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5601081, "numberOfFiles": 7, "fileFormat": "Data are BADG-CSV formatted." }, "timePeriod": { "ob_id": 8179, "startTime": "2018-05-24T23:00:00", "endTime": "2018-09-30T22:59:59" }, "resultQuality": { "ob_id": 3192, "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": "2018-11-22" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30098, "uuid": "a443dee426114fefb97c7dce738b916e", "short_code": "acq", "title": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI): Isoprene concentration measurements at Wytham Woods (UK) during the summer of 2018", "abstract": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI): Isoprene concentration measurements at Wytham Woods (UK) during the summer of 2018" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26485, "uuid": "742dfa25db5247c0a4306d297114e87d", "short_code": "proj", "title": "Biodiversity and Land Use Impacts on Tropical Forest Ecosystem Function (BALI)", "abstract": "Anthropogenic disturbance and land-use change in the tropics is leading to irrevocable changes in biodiversity and substantial shifts in ecosystem biogeochemistry. Yet, we still have a poor understanding of how human-driven changes in biodiversity feed back to alter biogeochemical processes. This knowledge gap substantially restricts our ability to model and predict the response of tropical ecosystems to current and future environmental change. There are a number of critical challenges to our understanding of how changes in biodiversity may alter ecosystem processes in the tropics; namely: (i) how the high taxonomic diversity of the tropics is linked to ecosystem functioning, (ii) how changes in the interactions among trophic levels and taxonomic groups following disturbance impacts upon functional diversity and biogeochemistry, and (iii) how plot-level measurements can be used to scale to whole landscapes. We have formed a consortium to address these critical challenges to launch a large-scale, replicated, and fully integrated study that brings together a multi-disciplinary team with the skills and expertise to study the necessary taxonomic and trophic groups, different biogeochemical processes, and the complex interactions amongst them.\r\n\r\nTo understand and quantify the effects of land-use change on the activity of focal biodiversity groups and how this impacts biogeochemistry, we will: (i) analyse pre-existing data on distributions of focal biodiversity groups; (ii) sample the landscape-scale treatments at the Stability of Altered Forest Ecosystems (SAFE) Project site (treatments include forest degradation, fragmentation, oil palm conversion) and key auxiliary sites (Maliau Basin - old growth on infertile soils, Lambir Hills - old growth on fertile soils, Sabah Biodiversity Experiment - rehabilitated forest, INFAPRO-FACE - rehabilitated forest); and (iii) implement new experiments that manipulate key components of biodiversity and pathways of belowground carbon flux. \r\n\r\nThe manipulations will focus on trees and lianas, mycorrhizal fungi, termites and ants, because these organisms are the likely agents of change for biogeochemical cycling in human-modified tropical forests. We will use a combination of cutting-edge techniques to test how these target groups of organisms interact each other to affect biogeochemical cycling. We will additionally collate and analyse archived data on other taxa, including vertebrates of conservation concern. The key unifying concept is the recognition that so-called 'functional traits' play a key role in linking taxonomic diversity to ecosystem function. We will focus on identifying key functional traits associated with plants, and how they vary in abundance along the disturbance gradient at SAFE. In particular, we propose that leaf functional traits (e.g. physical and chemical recalcitrance, nitrogen content, etc.) play a pivotal role in determining key ecosystem processes and also strongly influence atmospheric composition. Critically, cutting-edge airborne remote sensing techniques suggest it is possible to map leaf functional traits, chemistry and physiology at landscape-scales, and so we will use these novel airborne methods to quantify landscape-scale patterns of forest degradation, canopy structure, biogeochemical cycling and tree distributions. Process-based mathematical models will then be linked to the remote sensing imagery and ground-based measurements of functional diversity and biogeochemical cycling to upscale our findings over disturbance gradients. This project was funded under NERC grant NE/K016253/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 70666, 70667, 70668, 70669, 70670, 70671, 70672, 70673, 70674, 70675 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130008, 130009, 130011, 130013, 130012, 130007, 130014, 130005, 130010, 168901, 130015, 130006, 130016, 130017 ], "onlineresource_set": [] }, { "ob_id": 30099, "uuid": "53f8ae572f57473c897566f5c0165f95", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): surface temperature measurements from the University of Leeds Heitronics KT15.85 infra red radiative temperature sensor from an ice-floe site", "abstract": "This dataset contains surface temperature measurements from the University of Leeds Heitronics KT15.85 infra red radiative temperature sensor mounted at a 1.5-metre mast on an ice floe during the ice camp period of the joint Arctic Climate Across Scales (ACAS) and Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) projects. Both part of the Arctic Ocean 2018 (AO2018) expedition to the High Arctic.\r\n\r\nAO2018 took place in the Arctic from 1 August until 21 September 2018. These measurements were used to complement a suite of other observations taken during the expedition. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the Bolin Centre for Climate Research MOCCHA/AO2018 holdings.\r\n\r\nThese data consist of quality controlled measurements at 1s.\r\n\r\nThe UK participation of MOCCHA was funded by the Natural Environment Research Council (NERC, grant: NE/R009686/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-13T15:02:14", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the MOCCHA project data authors before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOCCHA, temperature, infra red", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-03-02T08:55:00", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2584, "bboxName": "", "eastBoundLongitude": 73.76, "westBoundLongitude": 3.98, "southBoundLatitude": 56.01, "northBoundLatitude": 89.9 }, "verticalExtent": null, "result_field": { "ob_id": 30100, "dataPath": "/badc/moccha/data/icebreaker-oden/leeds-kt15-ir-1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 70287371, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8180, "startTime": "2018-08-18T00:00:00", "endTime": "2018-09-14T00:00:00" }, "resultQuality": { "ob_id": 3381, "explanation": "Data quality were checked by the MOCCHA project data authors before supplying to CEDA.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-13" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30101, "uuid": "6327369a61284e2fb03c2f0ddafe941d", "short_code": "acq", "title": "Acquisition for: Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): surface temperature measurements from the University of Leeds Heitronics KT15.85 infra red radiative temperature sensor from an ice-floe site", "abstract": "Acquisition for: Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): surface temperature measurements from the University of Leeds Heitronics KT15.85 infra red radiative temperature sensor from an ice-floe site" }, "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": 29975, "uuid": "519bc609626e4f2a8e86a38e78018b23", "short_code": "proj", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "MOCCHA aims to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-level clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where icebreaker Oden was moored to an ice flow and drifted passively with the ice. \r\n\r\nMOCCHA is a joint project of the Universities of Stockholm (Sweden), Michigan (US), Leeds (UK), Leipzig/TROPOS (Germany), Oldenburg (Germany), Dalhousie (Canada), Lund (Sweden), Gothenburg (Sweden), California, Irvine (US), Paul Scherrer Institute (Switzerland), College of Charleston (USA), Virginia Institute of Marine Science (USA), Environment and Climate Change Canada, Alfred Wegener Institute (Germany), Bigelow Laboratory for Ocean Sciences (USA) and University College London (UK)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 54754, 56599, 56602 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29977, "uuid": "7f35130cb8c947bf9013594aec6b3043", "short_code": "coll", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "This dataset collection contains data collected from the Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) project. MOCCHA aimed to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-lewel clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where e icebreaker Oden was moored to an ice flow and drifted passively with the ice." } ], "responsiblepartyinfo_set": [ 130055, 130056, 130057, 130058, 130059, 130060, 130062, 130061, 168366, 130063 ], "onlineresource_set": [] }, { "ob_id": 30102, "uuid": "9fb19afbcb1644eabf7192bccc006a28", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): composite lidar wind profile data from the NCAS AMF Halo Doppler lidar on board Icebreaker Oden", "abstract": "This dataset contains composite lidar wind profile data from the NCAS AMF Halo Doppler lidar mounted on a motion stabilised platform on board the Swedish Icebreaker Oden during the joint Arctic Climate Across Scales (ACAS) and Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) projects - both part of the Arctic Ocean 2018 (AO2018) expedition to the High Arctic.\r\n\r\nAO2018 took place in the Arctic from 1 August until 21 September 2018. These measurements were used to complement a suite of other observations taken during the expedition. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the Bolin Centre for Climate Research MOCCHA/AO2018 holdings.\r\n\r\nWind profiles are derived from a motion stabilised HALO Photonics Doppler lidar using 6-beam Velocity-Azimuth-Display (VAD) scans at two fixed elevations, 30° and 75°. Data are available only where the backscatter signal to noise ratio is better than -16dB, lidar internal QC checks and quality criteria for the VAD algorithm are all passed. Each profile is derived from 6 2-second dwell beams. Wind profiles were measured every 15 minutes, with 2 consecutive scans at 30 and 75°, 30 sec apart. The consecutive scans were merged into one wind profile with a vertical resolution of 10 m using a normalised weighted mean function.\r\nDocumentation & validation of the motion stabilisation and derived winds can be found in: Achtert P, Brooks IM, Brooks BJ, Moat BI, Prytherch J, Persson POG, Tjernström M (2015) Measurement of wind profiles by motion-stabilised ship-borne Doppler lidar, Atmospheric Measurement Techniques, 8, 4993-5007. doi: 10.5194/amt-8-4993-2015\" ;\r\n\r\nThe UK participation of MOCCHA was funded by the Natural Environment Research Council (NERC, grant: NE/R009686/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-13T16:00:07", "updateFrequency": "notPlanned", "dataLineage": "Data were collected, quality controlled and prepared for archiving by the MOCCHA project data authors before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOCCHA, AMF, Halo, lidar", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-03-02T08:55:34", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2585, "bboxName": "", "eastBoundLongitude": 73.76, "westBoundLongitude": 3.98, "southBoundLatitude": 56.01, "northBoundLatitude": 89.9 }, "verticalExtent": null, "result_field": { "ob_id": 30103, "dataPath": "/badc/moccha/data/icebreaker-oden/ncas-lidar-dop-1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 68357777, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8181, "startTime": "2018-08-01T00:00:00", "endTime": "2018-09-17T00:00:00" }, "resultQuality": { "ob_id": 3382, "explanation": "Data quality were checked by the MOCCHA project data authors before supplying to CEDA.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-13" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30105, "uuid": "f5f3f1452f724caca7a9d613b34d5584", "short_code": "acq", "title": "Acquisition for: Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): composite lidar wind profile data from the NCAS AMF Halo Doppler lidar on board Icebreaker Oden", "abstract": "Acquisition for: Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): composite lidar wind profile data from the NCAS AMF Halo Doppler lidar on board Icebreaker Oden" }, "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": 29975, "uuid": "519bc609626e4f2a8e86a38e78018b23", "short_code": "proj", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "MOCCHA aims to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-level clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where icebreaker Oden was moored to an ice flow and drifted passively with the ice. \r\n\r\nMOCCHA is a joint project of the Universities of Stockholm (Sweden), Michigan (US), Leeds (UK), Leipzig/TROPOS (Germany), Oldenburg (Germany), Dalhousie (Canada), Lund (Sweden), Gothenburg (Sweden), California, Irvine (US), Paul Scherrer Institute (Switzerland), College of Charleston (USA), Virginia Institute of Marine Science (USA), Environment and Climate Change Canada, Alfred Wegener Institute (Germany), Bigelow Laboratory for Ocean Sciences (USA) and University College London (UK)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 21804, 24724, 24725, 24727, 24728, 24729, 24730 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29977, "uuid": "7f35130cb8c947bf9013594aec6b3043", "short_code": "coll", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "This dataset collection contains data collected from the Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) project. MOCCHA aimed to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-lewel clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where e icebreaker Oden was moored to an ice flow and drifted passively with the ice." } ], "responsiblepartyinfo_set": [ 130068, 130069, 130070, 130071, 130072, 130073, 130075, 130074, 130076, 168369, 130077 ], "onlineresource_set": [] }, { "ob_id": 30106, "uuid": "9405b2f8056947609da4bcc0bc88c5e8", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): SPRS Icebreaker Oden ship navigation data", "abstract": "This dataset contains ship navigation data, including speed over ground, course, heading etc, form the Swedish Polar Research Secretariat's (SPRS) Icebreaker Oden during the joint Arctic Climate Across Scales (ACAS) and Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) projects - both part of the Arctic Ocean 2018 (AO2018) expedition to the High Arctic.\r\n\r\nAO2018 took place in the Arctic from 1 August until 21 September 2018. These measurements were used to complement a suite of other observations taken during the expedition. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. Other cruise data may be available in the Bolin Centre for Climate Research MOCCHA/AO2018 holdings.\r\n\r\nThese data are provided as supportive data for use with the other datasets within this collection, helping to account for ship movement during the expedition for later data analysis. These data were prepared for archiving as NetCDF data at the Centre for Environmental Data Analysis (CEDA) by Jutta Vüllers, University of Leeds.\r\n\r\nThe UK participation of MOCCHA was funded by the Natural Environment Research Council (NERC, grant: NE/R009686/1) and involved instrumentation from the Atmospheric Measurement Facility of the UK's National Centre for Atmospheric Science (NCAS AMF).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-13T15:47:09", "updateFrequency": "notPlanned", "dataLineage": "Data were collected and prepared for archiving by the MOCCHA project data authors before upload to the Centre for Environmental Data Analysis (CEDA) for long term archiving.", "removedDataReason": "", "keywords": "MOCCHA, Ship, Navigation", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-03-03T09:11:51", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2586, "bboxName": "", "eastBoundLongitude": 73.76, "westBoundLongitude": 3.98, "southBoundLatitude": 56.01, "northBoundLatitude": 89.9 }, "verticalExtent": null, "result_field": { "ob_id": 30107, "dataPath": "/badc/moccha/data/icebreaker-oden/navigation", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 96041783, "numberOfFiles": 2, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8182, "startTime": "2018-07-20T00:00:00", "endTime": "2018-09-21T00:00:00" }, "resultQuality": { "ob_id": 3384, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-13" }, "validTimePeriod": null, "procedureAcquisition": null, "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": 29975, "uuid": "519bc609626e4f2a8e86a38e78018b23", "short_code": "proj", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "MOCCHA aims to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-level clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where icebreaker Oden was moored to an ice flow and drifted passively with the ice. \r\n\r\nMOCCHA is a joint project of the Universities of Stockholm (Sweden), Michigan (US), Leeds (UK), Leipzig/TROPOS (Germany), Oldenburg (Germany), Dalhousie (Canada), Lund (Sweden), Gothenburg (Sweden), California, Irvine (US), Paul Scherrer Institute (Switzerland), College of Charleston (USA), Virginia Institute of Marine Science (USA), Environment and Climate Change Canada, Alfred Wegener Institute (Germany), Bigelow Laboratory for Ocean Sciences (USA) and University College London (UK)." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 4385, 54754, 59041, 59042, 62025, 62026, 62027, 62028, 62029, 62030, 62031 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 29977, "uuid": "7f35130cb8c947bf9013594aec6b3043", "short_code": "coll", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA)", "abstract": "This dataset collection contains data collected from the Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) project. MOCCHA aimed to study aerosol processes, the contribution of marine microbiology to their formation and properties, and their impact on the life cycle of low-lewel clouds in the central Arctic as part of the Arctic Ocean 2018 (AO2018) expedition. AO2018 took place between 1 August and 21 September 2018, departing from and returning to Longyearbyen. The expedition was centered around a 4-week long intensive observation period, where e icebreaker Oden was moored to an ice flow and drifted passively with the ice." } ], "responsiblepartyinfo_set": [ 130082, 130083, 130084, 130085, 130086, 130087, 130089, 130088, 130090, 168367 ], "onlineresource_set": [] }, { "ob_id": 30114, "uuid": "4ca242208e904efe830af45f1697f730", "title": "A simulated Northern Hemisphere terrestrial climate dataset for the past 60,000 years (version 2)", "abstract": "We present a continuous land climate reconstruction dataset extending from 60 kyr before present to the pre-industrial period at 0.5deg resolution on a monthly timestep for 0degN to 90degN. It has been generated from 42 discrete snapshot simulations using the HadCM3B-M2.1 coupled general circulation model. We incorporate Dansgaard-Oeschger (DO) and Heinrich events to represent millennial scale variability, based on a temperature reconstruction from Greenland ice-cores, with a spatial fingerprint based on a freshwater hosing simulation with HadCM3B-M2.1. Interannual variability is also added and derived from the initial snapshot simulations. Model output has been downscaled to 0.5deg resolution (using simple bilinear interpolation) and bias corrected using either the University of East Anglia, Climate Research Unit observational data (for temperature, precipitation, windchill, and minimum monthly temperature), or the EWEMBI dataset (for incoming shortwave energy). Here we provide datasets for; surface air temperature, precipitation, incoming shortwave energy, wind-chill, snow depth (as snow water equivalent), number of rainy days per month, minimum monthly temperature, and the land-sea mask and ice fractions used in the simulations. The datasets are in the form of NetCDF files. The variables are represented by a set of 24 files that have been compressed into nine folders: temp, precip, down_sw, wind_chill, snow, rainy_days, tempmonmin, landmask and icefrac. Each file represents 2500 years. The landmask and ice fraction are provided annually, whereas the climate variables are given as monthly files equivalent to 30000 months, between the latitudes 0deg to 90degN at 0.5deg resolution. Each of the climate files therefore have the dimensions 180 (lat) x 720 (lon) x 30000 (month). We also provide an example subset of the temperature dataset, which gives decadal averages for each month for 0-2500 years.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-10T13:44:44", "updateFrequency": "notPlanned", "dataLineage": "The dataset has been produced using the HadCM3B coupled climate model, a version of the more commonly known HadCM3 that has been developed at the University of Bristol, and is outlined in detail in the study of Valdes et al., (2017). The original HadCM3 originated from the UK Met Office Hadley Centre. Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Climate, Model, HadCM3B, air temperature, precipitation", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-26T14:07:50", "doiPublishedTime": "2020-02-26T14:39:54.387052", "removedDataTime": null, "geographicExtent": { "ob_id": 2587, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": 0.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30111, "dataPath": "/badc/deposited2020/HadCM3B_60Kyr_Climate/data", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 331756060826, "numberOfFiles": 11, "fileFormat": "Data are gzipped, netCDF formatted." }, "timePeriod": null, "resultQuality": { "ob_id": 3387, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-26" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 26560, "uuid": "8a1587e2f3034eab8ef78e6b8ea4c5b8", "short_code": "comp", "title": "HadCM3B coupled climate model", "abstract": "The Hadley Centre Climate Model 3 Bristol (HadCM3B) is a coupled climate model consisting of a 3D dynamical atmosphere26 and ocean27 component. HadCM3B is a version of the more commonly known HadCM3 that has been developed at the University of Bristol" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 26558, "uuid": "8c1763ce40664a8399b35844b1684df0", "short_code": "proj", "title": "A coupled climate-vegetation-mammal-human model for simulating Late Quaternary megafaunal extinctions", "abstract": "The period 60,000-5,000 years ago saw the extinction of up to a thousand species of large vertebrates ('megafauna') across six continents. Understanding the cause of these extinctions is important for several reasons. It is the most recent substantial extinction event in the geological record; there is a background of detailed knowledge about environmental change against which to view the responses of the mammals; and humans are strongly implicated by many researchers as partial or exclusive causal agents. For all of these reasons, understanding the cause of the extinctions, and the reasons why some species survived while others did not, can provide a unique historical analogue for addressing the current biodiversity crisis. \r\n\r\nThe two main contenders for the megafaunal extinction are vegetation change driven by climate, and hunting by humans, either separately or in combination. Although the extinction was worldwide, we will focus on Europe, northern Asia and North America as these areas have the best data on the distributions and extinction of the mammals. We will first develop computer-based simulations of local climatic conditions across the study area; for the first time climate changes will be modelled on a year-by-year basis over the past 40,000 years. Using this information we will model vegetation types across the entire area. When climate changed, vegetation changed, but our model will be crucially more realistic than previous ones in that we will allow for the lags in vegetational response as plant species expand slowly across large areas (e.g. trees may have taken 1500 years to arrive in northern Europe when climate warmed after a long cold spell). In addition, the model estimates not only the type of vegetation but its productivity, i.e. amount of growth each year, of crucial importance to herbivorous mammals. \r\n\r\nMany of the mammals that went extinct (such as the woolly mammoth and wooly rhinoceros) were grazing species adapted to the productive grasslands of the last glaciation, and the predators that depended on them. Many of those that survived were browsing (woodland) mammals or those of mixed habitats. We will develop, for both victims and survivors, a biological profile for each species including their body weight, reproductive rate, and preferred foods. These will be determined from living relatives and from direct evidence such as wear on fossil teeth that indicates diet. We will also establish their climatic tolerance from the range of climates they occupied in the past.\r\n\r\nAdding the mammal fauna to the modelled climate and vegetation, and running the computer model from 40,000 years ago up to the present, the effect of climate changes on the vegetation, and the effect of both on each mammal species, will be evident. Moreover, the model will include feedback from the feeding activities of the mammals to the structure of the vegetation itself. A final element in the model is the addition of variable levels of human hunting, the distribution of people being determined from known archaeological sites. Analysis of all the data will determine if climatic and vegetational change, with or without the addition of hunting, are sufficient to account for the extinction of some megafauna and survival of others. This will be determined by comparing model results with the known pattern of range changes and extinction based on the fossil record. \r\n\r\nThe vegetation model that we develop would also allow prediction of likely responses to future climatic changes. Similarly, the climate simulations will be applicable to other processes (e.g. the changing extent of arctic permafrost). Our results will be directly relevant to various stakeholders, informing landscape management and biodiversity conservation strategies. We will ensure that they are communicated to such stakeholders, as well as to the scientific community and wider public.\r\n\r\nNERC grant: NE/P002536/1" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50559, 50561, 91909, 91910, 91911 ], "vocabularyKeywords": [], "identifier_set": [ 10696 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130110, 130112, 130115, 130114, 130113, 130116, 130111, 193402, 130117, 130118 ], "onlineresource_set": [ 36908, 36909 ] }, { "ob_id": 30115, "uuid": "d69dccc3b50041bb91a126b85e9154d0", "title": "Sentinel 5P: Suomi-NPP VIIRS cloud level 2 data", "abstract": "The S5P NPP Cloud product contains information on cloud and scene homogeneity for TROPOMI scenes, derived from operational products from the VIIRS (Visible Infra-red Imaging Radiometer Suite) on board the Suomi National Polar-orbiting Partnership (S-NPP) platform. S5P operates in loose formation orbit with NPP, so that measurements from VIIRS are well co-located with TROPOMI, with a time difference of about 3.5 minutes. There is no specific validation of auxiliary products within the S5P Mission Performance Centre.\r\n\r\nSentinel 5 Precursor (S5P) was launched on the 13th October 2017 carrying the TROPOspheric Monitoring Instrument (TROPOMI). The TROPOMI instrument onboard S5P is a nadir-viewing, imaging spectrometer covering wavelength bands between the ultraviolet and the shortwave infrared. The instrument uses passive remote sensing techniques to attain its objective by measuring, at the Top Of Atmosphere (TOA), the solar radiation reflected by and radiated from the earth.\r\n\r\nThe S5P level 2 methane product is dependent on having information on cloud occurrence at spatial resolution finer than that achievable from TROPOMI itself. This information is also useful for other purposes, including assessing the influence of cloud on other L2 products and issues related to spatial co-registration. NPP-Cloud was therefore developed as a level 2 auxiliary product to describe cloud in the TROPOMI field of view (FOV), using co-located observations of VIIRS on the U.S. S-NPP. \r\n\r\nThe main information contained in the S5P-NPP product is:\r\n\r\n1. A statistical summary for each S5P FOV of the NPP-VIIRS L2 Cloud Mask (VCM).\r\n2. The mean and standard deviation of the sun-normalised radiance in a number of VIIRS moderate resolution bands.\r\nThis information is provided for three S5P spectral bands (to account for differences in spatial sampling).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-06-26T18:43:00", "updateFrequency": "", "dataLineage": "Data collected and prepared by European Space Agency (ESA). Downloaded from the Collaborative Hub for use by CEDA users.", "removedDataReason": "", "keywords": "Sentinel, ESA, VIIRS, cloud, Suomi-NPP, S5P", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2022-08-12T08:00:25", "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": 30116, "dataPath": "/neodc/sentinel5p/data/L2_NP/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 32715044142843, "numberOfFiles": 202744, "fileFormat": "These data are in NetCDF format." }, "timePeriod": { "ob_id": 9000, "startTime": "2018-05-04T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 1, "explanation": "See dataset associated documentation", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2012-08-15" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 37710, "uuid": "2e9c4e2cab344e60a2cf7f02759daa8a", "short_code": "cmppr", "title": "Composite process for the Visible Infra-red Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (SNPP), Top of Atmosphere (TOA) level 2 (L2) S5P-NP-BDx cloud products.", "abstract": "This process generates auxiliary data for observations collected by the Copernicus Sentinel 5 Precursor Tropospheric Monitoring Instrument (S5P/TROPOMI, see catalogue record link in documentation). The auxiliary data are cloud information relevant to each TROPOMI point of view and are produced by the VIIRS instrument on the SNPP platform (see catalogue link in documentation). \r\n\r\nComputed NPP-VIIRS data (consisting of a VCM/ECM cloud mask product) is used for cloud screening in order to enhance the accuracy of the methane (CH4) column retrieval algorithm on Sentinel 5P data, due to its superior spatial resolution and spectral band coverage compared to S5P.\r\n\r\nFor more information on the cloud retrieval algorithms used see the documentation on the Sentinel webpage: https://sentinel.esa.int/documents/247904/0/Sentinel-5P-TROPOMI-ATBD-Clouds/29d45625-f34c-45bd-ac85-985eb9250a47. These include ICFA (Initial Cloud Fitting Algorithm), FRESCO (Fast REtrieval Scheme for Clouds from the Oxygen band), SACURA (Semi-Analytical CloUd Retrieval Algorithm), OCRA (Optical Cloud Recognition Algorithm) and ROCINN (Retrieval of Cloud Information using Neural Networks)." }, "imageDetails": [ 148 ], "discoveryKeywords": [], "permissions": [ { "ob_id": 2586, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 49, "licenceURL": "https://sentinel.esa.int/documents/247904/690755/Sentinel_Data_Legal_Notice", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 12321, "uuid": "7896ea1117dc4fa9bb95485ca9b1c6be", "short_code": "proj", "title": "Copernicus Programme", "abstract": "Copernicus, formerly known as the Global Monitoring for Environment and Security (GMES) programme, is headed by the European Commission (EC) in partnership with the European Space Agency (ESA). Within the Copernicus Space Component, ESA is developing a series of Sentinel satellite missions. Data from the Sentinel missions, as well as from Contributing Missions from other space agencies, are made freely available through a unified ground segment. Each Sentinel mission is comprised of a constallation of two or more satellites to fulfil the timeliness and reliability requirements of the Copernicus Services environmental monitoring and civil security activities. As well as operational and monitoring capabilities, the Sentinel missions will provide a wealth of Earth Observation data for scientific exploitation. The Sentinel 1 mission provides all weather, day and night radar imagery with scientific applications in sea-ice measurements, biomass observations and earthquake analysis. Sentinel 2 is a high resolution imaging mission to provide imagery of vegetation, soil and water cover, inland waterways and coastal areas. Sentinel 3 is a multi-instrument mission to measure sea-surface topography, sea- and land-surface temperature, ocean colour and land colour with high-end accuracy and reliability. Sentinel 4 is devoted to atmospheric monitoring and will be flown on a Meteosat Third Generation-Sounder (MTG-S) satellite in geostationary orbit. Sentinel 5 will monitor the atmosphere from polar orbit on board a MetOp Second Generation satellite. The Sentinel 5 precursor satellite mission is being developed to reduce data gaps between Envisat, in particular the Sciamachy instrument, and the launch of Sentinel 5. The Sentinel 5 mission will be dedicated to atmospheric monitoring. Sentinel 6 carries a radar altimeter to measure global sea-surface height, primarily for operational oceanography and for climate studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 55535, 66624, 66625, 66678, 66715, 66716, 66973, 66974, 66980, 66992, 66995, 67368, 67369, 67370, 67371, 67372, 67373, 67374, 67375, 67376, 67377, 67378, 67379, 67380, 67381, 67382, 67383, 67384, 67385, 67386, 67387, 67388, 67389, 67390, 67391, 67392, 67393, 67394, 67395, 67396, 67397, 67398, 67399, 67400, 67401, 67402, 67403, 67404, 67405, 67406, 67407, 67408, 67409, 67410, 67411, 67412, 67413, 67414, 67415, 67416, 67417, 67418, 67419, 67420, 67421, 67422, 67423, 67424, 67425, 67426, 92109, 92110, 92111, 92112, 92113, 92114, 92115, 92116, 92117, 92118, 92119, 92120, 92121, 92122, 92123, 92124, 92125, 92126, 92127, 92128, 92129, 92130, 92131, 92132, 92133, 92134, 92135, 92136, 92137, 92138 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30129, "uuid": "3b0630c7fa264164868d4da5c9f90bed", "short_code": "coll", "title": "National Centre for Earth Observation (NCEO) Third Party Data", "abstract": "The National Centre for Earth Observation (NCEO) Third Party data contains a broad range remotely sensed data acquired by satellite for use by the Earth Observation Scientific community supported by NCEO. The Centre for Environmental Data Analysis (CEDA) has archived and provides access to extensive Earth observation datasets under strict licensing conditions. Please see the individual dataset records for conditions of use." }, { "ob_id": 26459, "uuid": "6accd46663bc4669afaac418f2bf498e", "short_code": "coll", "title": "Sentinel 5 Precursor: Level 2 data", "abstract": "Need some info!" } ], "responsiblepartyinfo_set": [ 130127, 130131, 130128, 130129, 130130, 130132, 130134, 146236, 130135, 130133, 146237, 180862 ], "onlineresource_set": [ 36922, 36923, 36925, 36924, 52718, 52719 ] }, { "ob_id": 30120, "uuid": "8351ed155b134155848d03a7cdce9f02", "title": "Ozone eddy covariance measurements at Penlee Point Atmospheric Observatory, April-May 2018", "abstract": "This dataset contains wind, temperature, pressure, humidity and ozone mixing ratio, given as the 20-minute mean of 10 Hz (ozone and wind) or 0.25 Hz (pressure, humidity and temperature) raw data. Ozone flux values calculated from the raw 10 Hz wind and ozone data are presented as mass fluxes and as molar fluxes. Deposition velocity is calculated from the molar flux.\r\n\r\nData relevant to the data selection are also included, such as ozone variability, instrument sensitivity, wind stationarity and wind speed and direction. The Penlee Point Atmospheric Observatory (PPAO) site is best suited to observing fluxes over water. The north-west sector contains the sloping headland on which the observatory is built, making flux observations in this sector of limited value.\r\n\r\nThe footprint area (where the measured flux is occurring) varies with wind conditions, but generally 90% of flux contribution occurs within 2-3 km of the observatory. The building itself sits 30-60m from the coast, depending on tide.\r\n\r\nMeasurements were taken from 2018/04/10 - 2018/05/21 using a Gill WindMaster Pro sonic anemometer, a Gill MetPak Pro, and a high frequency chemiluminescence ozone detector from Eco Physics. A 2B 205 ozone monitor was used as a reference for the high frequency ozone instrument. Data collection was managed by David Loades (University of York), Thomas Bell (Plymouth Marine Laboratory) and Mingxi Yang (Plymouth Marine Laboratory). \r\n\r\nData are missing where fluxes couldn't be calculated, most commonly due to heavy rain disrupting the anemometer or power outages.\r\n\r\nThese data were collected for the Natural Environment Research Council (NERC) project Iodide in the ocean:distribution and impact on iodine flux and ozone loss.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-02-27T09:07:47", "updateFrequency": "notPlanned", "dataLineage": "10 Hz data logged at the Penlee Point Atmospheric. High frequency data were despiked and detrended, and double rotation was applied to the wind vectors for flux calculation. The data were then sent to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "Ozone, eddy, Penlee Point", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-02-28T15:33:12", "doiPublishedTime": "2020-07-13T16:25:42", "removedDataTime": null, "geographicExtent": { "ob_id": 2588, "bboxName": "", "eastBoundLongitude": -4.189, "westBoundLongitude": -4.189, "southBoundLatitude": 50.318, "northBoundLatitude": 50.318 }, "verticalExtent": null, "result_field": { "ob_id": 30121, "dataPath": "/badc/deposited2019/sea-surface-iodide-ozone/data/v1", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1287361, "numberOfFiles": 2, "fileFormat": "Data are BADC-CSV formatted." }, "timePeriod": { "ob_id": 8183, "startTime": "2018-04-10T00:00:00", "endTime": "2018-05-22T00:00:00" }, "resultQuality": { "ob_id": 3388, "explanation": "All data sufficiently present are recorded here. Parameters commonly used in flux data selection (stationarity of wind and ozone, friction velocity, wind speed). Data used for investigating oceanic ozone deposition to the south west are flagged.\n\nNote the headland to the north-west rises steeply to above the level of the flux tower, making flux measurements in this region of limited quality.", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-27" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30122, "uuid": "4f1f390f18884a42a06999adf171f2e2", "short_code": "acq", "title": "Acquisition for: Ozone eddy covariance measurements at Penlee Point Atmospheric Observatory (PPAO), Apr-May 2018", "abstract": "Acquisition for: Ozone eddy covariance measurements at Penlee Point Atmospheric Observatory (PPAO), Apr-May 2018" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 27022, "uuid": "bdf2bea40e1e4c7fb9800d909aeb0703", "short_code": "proj", "title": "Iodide in the ocean:distribution and impact on iodine flux and ozone loss", "abstract": "Iodide in the ocean:distribution and impact on iodine flux and ozone loss is a NERC funded project (NE/N009983/1) which aimed to quantify the dominant controls on the sea surface iodide distribution and improve parameterisation of the sea-to-air iodine flux and of ozone deposition. This was achieved through a combination of laboratory experiments, field measurements and ocean and atmospheric modelling." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 91506, 91507, 91508, 91509, 91510, 91511, 91512, 91513, 91514, 91515, 91516, 91517, 91518, 91519, 91520, 91521, 91522, 91523, 91524, 91525, 91526, 91527, 91528, 91529, 91530, 91531, 91532, 91533, 91534, 91535, 91536, 91537, 91538, 91539, 91540, 91541, 91542, 91543, 91544, 91545, 91546, 91547, 91548, 91549 ], "vocabularyKeywords": [], "identifier_set": [ 10728 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130152, 130153, 130154, 130155, 130156, 130158, 130157, 130151, 130159, 130160, 130161, 130162 ], "onlineresource_set": [ 87683 ] }, { "ob_id": 30126, "uuid": "3534bbf43fa14e40bc61944eaf664511", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged methane from Sentinel-5P, generated with the WFM-DOAS algorithm, version 1.2", "abstract": "This product is the column-average dry-air mole fraction of atmospheric methane, denoted XCH4. It has been retrieved from radiance measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on the Sentinel-5 Precursor satellite in the 2.3 µm spectral range of the solar spectral range, using the Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS or WFMD) retrieval algorithm. This dataset is also referred to as CH4_S5P_WFMD.\r\n\r\nThe WFMD algorithm is based on iteratively fitting a simulated radiance spectrum to the measured spectrum using a least-squares method. The algorithm is very fast as it is based on a radiative transfer model based look-up table scheme. The product is limited to cloud-free scenes on the Earth's day side.\r\n\r\nThis data was produced as part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2020-02-27T16:14:56", "updateFrequency": "", "dataLineage": "Data were processed by the ESA CCI GHG project team and supplied to CEDA as part of the ESA CCI Open Data Portal Project.", "removedDataReason": "", "keywords": "ESA, CCI, satellite, Sentinel-5 Precursor, TROPOMI, atmosphere, methane, carbon monoxide", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2020-03-31T16:11:33", "doiPublishedTime": "2020-04-01T08:04:32", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30125, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CH4_S5P_WFMD/v1.2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 16714250286, "numberOfFiles": 376, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8184, "startTime": "2017-11-10T00:00:00", "endTime": "2018-12-31T23:59:59" }, "resultQuality": { "ob_id": 3389, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 54763, 54764, 55532, 55533, 63576, 66973, 66974, 68636, 68637, 69622, 69623, 69628, 69634, 69635, 74642, 74644, 74645, 74648, 74649, 74650, 74651, 74653, 74654, 74655, 74658, 74659, 74660, 79736 ], "vocabularyKeywords": [ { "ob_id": 10664, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_ghg", "resolvedTerm": "greenhouse gases" }, { "ob_id": 11151, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_tropomi", "resolvedTerm": "TROPOMI" }, { "ob_id": 11152, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_sentinel5p", "resolvedTerm": "Sentinel-5P" } ], "identifier_set": [ 10706 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 130165, 130167, 130168, 130169, 130171, 130172, 130173, 130170, 130582, 130583, 130584, 130585 ], "onlineresource_set": [ 36932, 36940, 36934, 36933, 36984 ] }, { "ob_id": 30130, "uuid": "be7c7d11ab7f4a358002d53216214163", "title": "Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA): lidar radial velocity and backscatter data for vertical stare mode from the NCAS AMF Halo Doppler lidar on board Icebreaker Oden", "abstract": "This dataset contains lidar radial velocity and backscatter data for vertical stare mode from the NCAS AMF Halo Doppler lidar mounted on a motion stabilised platform on board the Swedish Icebreaker Oden during the joint Arctic Climate Across Scales (ACAS) and Microbiology-Ocean-Cloud Coupling in the High Arctic (MOCCHA) projects. Both projects are part of the Arctic Ocean 2018 (AO2018) expedition to the High Arctic.\r\n\r\nAO2018 took place in the Arctic from 1 August until 21 September 2018. These measurements were used to complement a suite of other observations taken during the expedition. Those of the UK contribution, as well as selected other data, are available within the associated data collection in the Centre for Environmental Data Analysis (CEDA) archives. 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To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. 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ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 14474, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15024, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15812, 15813, 15816, 15824, 15836, 15840, 15845, 15846, 15847, 15848, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20676, 20677, 20685, 20687, 20688, 20689, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 21041, 21042, 21043, 21044, 21045, 21046, 21049, 22364, 22461, 22462, 22464, 22465, 22466, 22467, 22468, 22469, 22470, 22472, 22847, 23620, 25773, 25777, 26141, 26149, 28558, 28559, 28561, 28562, 28563, 28564, 28565, 28566 ], "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": 30143, "uuid": "6f44f8fed1b3490ca8b112a6074dd00d", "short_code": "coll", "title": "ACSIS: in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for ACSIS FAAM Aircraft Projects." }, { "ob_id": 24737, "uuid": "31ae96f9cfc54ef9a38638f8723a1d17", "short_code": "coll", "title": "ACSIS: Data collected during the The North Atlantic Climate System Integrated Study.", "abstract": "This data collection includes a range of data collected for The North Atlantic Climate System Integrated Study: ACSIS, including: In-situ airborne observations by the FAAM BAE-146 aircraft, groundbased air composition measurements from Penlee Observatory, and Atlantic Ocean Sea Surface Temperature (SST) studies." } ], "responsiblepartyinfo_set": [ 130353, 130354, 130355, 130356, 130359, 130360, 130361, 130362, 130357, 130358 ], "onlineresource_set": [] }, { "ob_id": 30173, "uuid": "e8c2a0e24726480fb65b34d03a8b7d06", "title": "FAAM C229 ACSIS-6 Transit flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft", "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for ACSIS-6 FAAM Aircraft Project project.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2021-02-17T20:16:59", "updateFrequency": "asNeeded", "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": "ACSIS-6, FAAM, airborne, atmospheric measurments", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-03-06T11:53:15", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2606, "bboxName": "", "eastBoundLongitude": -0.37401050329208374, "westBoundLongitude": -8.6859769821167, "southBoundLatitude": 41.146480560302734, "northBoundLatitude": 52.240821838378906 }, "verticalExtent": null, "result_field": { "ob_id": 30172, "dataPath": "/badc/faam/data/2020/c229-feb-14", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 2573404550, "numberOfFiles": 28, "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": 8205, "startTime": "2020-02-14T14:14:21", "endTime": "2020-02-14T17:18:00" }, "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": null, "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": 24717, "uuid": "7e92f3a40afc494f9aaf92525ebb4779", "short_code": "proj", "title": "The North Atlantic Climate System Integrated Study: ACSIS", "abstract": "Major changes are occurring across the North Atlantic (NA) climate system: in ocean and atmosphere temperatures and circulation, in sea ice thickness and extent, and in key atmospheric constituents such as ozone, methane and aerosols. Many observed changes are unprecedented in instrumental records. Changes in the NA directly affect the UK’s climate, weather and air quality, with major economic impacts on agriculture, fisheries, water, energy, transport and health. The NA also has global importance, since changes here drive changes in climate, hazardous weather and air quality further afield, such as in North America, Africa and Asia.\r\n\r\nACSIS (the North Atlantic Climate System Integrated Study) was an integrated programme of sustained observations, synthesis, and numerical modelling designed to address the overarching objective of enhancing the UK's capability to detect, attribute and predict changes in the North Atlantic (NA) Climate System, comprising: the North Atlantic Ocean, the atmosphere above it including its composition, and interactions with Arctic Sea Ice and the Greenland Ice Sheet. Specific objectives are:\r\n1. To provide the UK science community with sustained observations, data syntheses, leading-edge numerical simulations, and analysis tools, to facilitate world-class research on changes in the NA climate system and their impacts.\r\n2. To provide a quantitative, multivariate, description of how the NA climate system is changing.\r\n3. To determine the primary drivers and processes that are shaping change in the NA climate system now and will shape change in the near future.\r\n4. To determine the extent to which future changes in the NA climate system are predictable.\r\nACSIS enabled and delivered research to address the following research questions:\r\nRQ1. How have changes in natural and anthropogenic emissions and atmospheric circulation combined to shape multiyear trends in NA atmospheric composition and radiative forcing?\r\nRQ2. How have natural variability and radiative forcing combined to shape multi- year trends in the NA physical climate system?\r\nRQ3. To what extent are changes in the NA climate system predictable on multi-year timescales?\r\nACSIS was a partnership between six NERC centres (NCAS, NOC, BAS, NCEO, CPOM, PML) and the UK Met Office, exploiting the partners' unique capabilities in observing and simulating the atmosphere including its composition, the ocean, the cryosphere, and the fully coupled climate system.\r\nThe observational component brought together records from Earth-based (e.g. Cape Verde observatory, FAAM missions, RAPID, Argo, OSNAP) and spacebased (e.g. Cryosat, MetOP) platforms with a focus on the sustained observations that are necessary to measure changes on multi-year timescales.\r\nACSIS worked closely with the NERC-Met Office UKESM programme on Earth System Modelling, and contributed to and benefited from UK participation in international observing programmes such as UK-US RAPID, EU ATLANTOS and Global Atmospheric Watch, and modelling programmes such as CMIP6 and EU PRIMAVERA.\r\nThe legacy of ACSIS includes: new long-term multivariate observational datasets and syntheses; new modelling capabilities and simulations with unprecedented fidelity. ACSIS provided advances in understanding and predicting changes in the NA climate system that can be exploited in further research and related activities, for example to assess the impact of these changes on the UK and other countries - e.g. in terms of the consequences for hazardous weather risk, the environment and businesses. ACSIS outputs will also inform policy on climate change adaptation and air quality.\r\nACSIS was fully funded for five years (2016-2021)by the Natural Environment Research Council (NERC) through National Capability Long Term Science Multiple Centre (NC LTS-M) (grant NE/N018028/1) which aimed to encourage its research centres to work closely together to tackle major scientific and societal challenges. ACSIS is one of the projects funded through this new way of allocating national capability funding, designed to enable more ambitious science than any single research organisation could provide." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 14474, 14836, 14837, 14838, 14840, 14841, 14842, 14843, 14845, 14846, 14847, 14848, 14849, 14850, 14851, 14852, 14853, 14854, 14855, 14857, 14859, 14860, 14861, 14863, 14864, 14865, 14866, 14868, 14869, 14870, 14871, 14872, 14873, 14874, 14875, 14876, 14877, 14878, 14880, 14882, 14883, 14885, 14887, 14890, 14893, 14894, 14895, 14900, 14901, 14905, 14906, 14912, 14916, 14917, 14919, 14921, 14924, 14927, 14928, 14935, 14946, 15024, 15324, 15325, 15336, 15337, 15338, 15339, 15342, 15343, 15344, 15812, 15813, 15816, 15824, 15836, 15840, 15845, 15846, 15847, 15848, 16569, 16658, 17108, 17109, 18977, 20656, 20657, 20658, 20659, 20660, 20661, 20662, 20663, 20664, 20665, 20666, 20667, 20668, 20669, 20670, 20671, 20672, 20673, 20676, 20677, 20685, 20687, 20688, 20689, 20691, 20692, 20693, 20694, 20695, 20696, 20697, 21041, 21042, 21043, 21044, 21045, 21046, 21049, 22364, 22461, 22462, 22464, 22465, 22466, 22467, 22468, 22469, 22470, 22472, 22847, 23620, 25773, 25777, 26141, 26149, 28558, 28559, 28561, 28562, 28563, 28564, 28565, 28566 ], "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": 30143, "uuid": "6f44f8fed1b3490ca8b112a6074dd00d", "short_code": "coll", "title": "ACSIS: in-situ airborne observations by the FAAM BAE-146 aircraft", "abstract": "In-situ airborne observations by the FAAM BAE-146 aircraft for ACSIS FAAM Aircraft Projects." }, { "ob_id": 24737, "uuid": "31ae96f9cfc54ef9a38638f8723a1d17", "short_code": "coll", "title": "ACSIS: Data collected during the The North Atlantic Climate System Integrated Study.", "abstract": "This data collection includes a range of data collected for The North Atlantic Climate System Integrated Study: ACSIS, including: In-situ airborne observations by the FAAM BAE-146 aircraft, groundbased air composition measurements from Penlee Observatory, and Atlantic Ocean Sea Surface Temperature (SST) studies." } ], "responsiblepartyinfo_set": [ 130363, 130364, 130365, 130366, 130369, 130370, 130371, 130372, 130367, 130368 ], "onlineresource_set": [] }, { "ob_id": 30176, "uuid": "2c0b0a1b24c1489580290255f434e0d7", "title": "Weybourne Atmospheric Observatory: Radon concentration measurements in air from the ANSTO 1500 L detector", "abstract": "This dataset contains Radon concentration measurements in air from the ANSTO 1500 L detector at the Weybourne Atmospheric Observatories. \r\n\r\nThe Weybourne Atmospheric Observatory (WAO) is a Regional station in the Global Atmosphere Watch (GAW) programme of the World Meteorological Organization (WMO). It is situated on the North Norfolk coast (52°57’02’’N, 1°07’19’’E, 15 m asl).", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2020-03-10T10:43:51", "updateFrequency": "notPlanned", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Weybourne Atmospheric Observatory, radon", "publicationState": "preview", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": null, "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 893, "bboxName": "Weybourne Atmospheric Observatory", "eastBoundLongitude": 1.1219, "westBoundLongitude": 1.1219, "southBoundLatitude": 52.9504, "northBoundLatitude": 52.9504 }, "verticalExtent": null, "result_field": { "ob_id": 30177, "dataPath": "/badc/weybourne/data/radon", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 1900399, "numberOfFiles": 24, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8206, "startTime": "2018-04-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3392, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-03-10" }, "validTimePeriod": null, "procedureAcquisition": { "ob_id": 30175, "uuid": "a957b567abf6404a81afeb13ac4bd739", "short_code": "acq", "title": "Weybourne Atmospheric Observatory: Radon concentration measurements in air from the ANSTO 1500 L detector", "abstract": "Weybourne Atmospheric Observatory: Radon concentration measurements in air from the ANSTO 1500 L detector" }, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 56 ], "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": [], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 53936, 53937, 53938, 53939, 53940, 53941, 53942, 53943, 53944, 53947, 62038 ], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130387, 130388, 130389, 130390, 130392, 130393, 130394, 130391 ], "onlineresource_set": [] }, { "ob_id": 30178, "uuid": "b06213c3f3934a689f89ab22aa50e471", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column averaged carbon dioxide from OCO-2 generated with the FOCAL algorithm, version 08", "abstract": "This dataset contains column-average dry-air mole fractions of atmospheric carbon dioxide (XCO2), using the fast atmospheric trace gas retrieval for OCO2 (FOCAL-OCO2). The FOCAL-OCO2 algorithm which has been setup to retrieve XCO2 by analysing hyper spectral solar backscattered radiance measurements from NASA's Orbiting Carbon Observatory 2 (OCO-2) satellite. FOCAL includes a radiative transfer model which has been developed to approximate light scattering effects by multiple scattering at an optically thin scattering layer. This reduces the computational costs by several orders of magnitude. FOCAL's radiative transfer model is utilised to simulate the radiance in all three OCO-2 spectral bands allowing the simultaneous retrieval of CO2, H2O, and solar induced chlorophyll fluorescence. The product is limited to cloud-free scenes on the Earth's day side. This dataset is also referred to as CO2_OC2_FOCA.\r\n\r\nThis version of the data was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).\r\n\r\nWhen citing this dataset, please also cite the following peer-review publications:\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, V.Rozanov, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 1: Radiative Transfer and a Potential OCO-2 XCO2 Retrieval Setup, Remote Sensing, 9(11), 1159; doi:10.3390/rs9111159, 2017\r\n\r\nM.Reuter, M.Buchwitz, O.Schneising, S.Noël, H.Bovensmann and J.P.Burrows: A Fast Atmospheric Trace Gas Retrieval for Hyperspectral Instruments Approximating Multiple Scattering - Part 2: Application to XCO2 Retrievals from OCO-2, Remote Sensing, 9(11), 1102; doi:10.3390/rs9111102, 2017", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2024-03-09T03:22:27", "updateFrequency": "notPlanned", "dataLineage": "This version of the data was produced as part of the European Space Agency's (ESA) \r\nClimate Change Initiative (CCI) Greenhouse Gases (GHG) project (GHG-CCI+, http://cci.esa.int/ghg)\r\nand got co-funding from the Univ. Bremen and EU H2020 projects CHE (grant agreement no. 776186) and VERIFY (grant agreement no. 776810).", "removedDataReason": "", "keywords": "ESA, CCI, satellite, CO2", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "", "status": "superseded", "dataPublishedTime": "2020-03-31T16:10:44", "doiPublishedTime": "2020-04-01T08:04:07", "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30179, "dataPath": "/neodc/esacci/ghg/data/cci_plus/CO2_OC2_FOCA/v8.0", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 5860772063, "numberOfFiles": 1312, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8207, "startTime": "2015-01-01T00:00:00", "endTime": "2018-12-30T23:59:59" }, "resultQuality": { "ob_id": 3389, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-02-28" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": null, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2564, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 34, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_ghg_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13295, "uuid": "f0c66ffa30514d2daee821286a014b16", "short_code": "proj", "title": "ESA Greenhouse Gases Climate Change Initiative Project", "abstract": "The European Space Agency Greenhouse Gases Climate Change Initiative (GHG CCI) project is one of several projects of ESA's Climate Change Initiative (CCI), which will deliver various Essential Climate Variables (ECVs)\r\n\r\nCarbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases (GHGs) and a focus of international research activities related to a better understanding of the carbon cycle (see, for example, the Global Carbon Project (GCP)).\r\n \r\nWithin the GHG-CCI project the focus is on satellite data. Satellite observations combined with modelling can add important missing global information on regional CO2 and CH4 (surface) sources and sinks required for better climate prediction. The GHG CCI project started on the 1st September 2010." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 1050, 1635, 1636, 11820, 11831, 11841, 11842, 11846, 11848, 23212, 28592, 28593, 28594, 28595, 28596, 28597 ], "vocabularyKeywords": [ { "ob_id": 11150, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/platform/plat_oco2", "resolvedTerm": "OCO-2" }, { "ob_id": 11149, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/sensor/sens_oco", "resolvedTerm": "OCO" }, { "ob_id": 10664, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_ghg", "resolvedTerm": "greenhouse gases" } ], "identifier_set": [ 10705 ], "observationcollection_set": [ { "ob_id": 12808, "uuid": "0508f3dd991144aa80346007a415fb07", "short_code": "coll", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci) dataset collection", "abstract": "The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). \r\n\r\nThe products have each been generated from individual sensors, a single merged product not having yet been created \"combining\" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. \r\n\r\nTypically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. \r\n\r\nFor further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section." } ], "responsiblepartyinfo_set": [ 130396, 130397, 130399, 130400, 130401, 130402, 130395, 130398, 130586, 130587, 130588, 130589 ], "onlineresource_set": [ 36941, 36942, 36985, 37065, 37066, 36943, 37067, 37068, 37069, 95000, 95001 ] }, { "ob_id": 30184, "uuid": "33a2a4b2c2224c5c9a1d17aca747fa19", "title": "Methane Emissions in a Chemistry-Climate Model: feedbacks and climate response", "abstract": "This dataset contains methane concentrations from a chemistry-climate model, focusing on year 2000 and year 2100. The present-day forcings were used for the 2000 simulation, while for year 2100 the Representative Concentration Pathway (RCP) RCP8.5 pathway were used. All experiments were run as perpetual timeslice experiments and are global model simulations made using the Met Office Unified Model at vn7.3 based on the HadGEM3-A science configuration. The model was run in atmosphere-only mode with a horizontal resolution of 2.5 degree latitude by 3.75 degree longitude, 60 vertical levels up to 84 km, and prescribed sea surface temperatures and sea ice extents. SST, sea ice and other forcings from earlier model experiments were used, e.g. Banerjee et al. (doi:/10.5194/acp-14-9871-2014). \r\n\r\nFor the year 2000 experiments, greenhouse gas, ozone depleting substances and ozone precursor emissions are taken from the Coupled Model Intercomparison Project - Phase 5 (CMIP5) reference forcings, as in Lamarque, 2010 (doi.org/10.5194/acp-10-7017-2010) The data comprise a series of separate experiments designed to test the performance of methane emissions in this model and to compare against the default that uses a prescribed concentration at the surface, with a view to assessing the performance of the more physically realist emissions treatment. \r\n\r\nFor year 2000, the model was run for three experiments: the first (BASE) experiments employ a set of emissions derived from EDGAR v4 to describe anthropogenic methane emissions, with biognenic emissions taken from the chemistry-transport model (CTM) intercomparison experiment (TransCom-CH4) paper of Patra et al, (DOI:10.5194/acp-11-12813-2011), a second set of experiments used identical emissions to BASE except that CO emissions were increased by 50% globally from the emissions of Lamarque, this is called Delta_CO, and a third year 2000 experiment used CMIP5 methane emissions from Lamarque (as above) rather than EDGAR and biogenic emissions derived from Melton et al. (https://doi.org/10.5194/bg-10-753-2013) rather than Patra. These experiments test the model skill in simulating methane based on the model treatment of emissions and establish the sensitivity to emissions. \r\n\r\nThree future climate experiments were also performed using climate forcings appropriate to year 2100 following the RCP8.5 pathway. We looked at the climate drivers in turn that arise as the climate changes. In the first, DELTA_CC, climate forcings were adjusted from greenhouse gases to year 2100 values, but kept methane and other anthropogenic forcings at year 2000 values, a second experiment, DELTA_CH4, adjusted CH4 to Year 2100 values from the emissions database as above, a third was an all-forcings experiments in which greenhouse gases, methane and ozone precursors were adjusted to RCP8.5 levels. This gave three separate experiments which explore how methane responds to these changes in the climate drivers.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-13T14:02:08", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the Met Office Unified Model (UM) at N48 horizontal resolution over global domain. The data were sent by the data provider to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "methane, model, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-03-23T11:13:32", "doiPublishedTime": "2020-03-23T14:31:21.215740", "removedDataTime": null, "geographicExtent": { "ob_id": 2608, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30185, "dataPath": "/badc/deposited2020/methane-chemistry-climate-model/data/UM-UKCA_vn73_Y2K_RCP8_5_CH4", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 6459897794, "numberOfFiles": 23, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8210, "startTime": "2000-01-21T00:00:00", "endTime": "2100-01-01T23:59:59" }, "resultQuality": { "ob_id": 3395, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-03-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30187, "uuid": "00e79957023446628a4ec3da86baaa7b", "short_code": "comp", "title": "Met Office operational unified model (UM) deployed on unknown computer", "abstract": "Met Office operational unified model (UM) deployed on unknown computer" }, "procedureCompositeProcess": null, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30186, "uuid": "80f56196c4184a77a581b26591d7348b", "short_code": "proj", "title": "ACCI- Atmospheric Chemistry-Climate Interactions", "abstract": "ACCI was a ERC funded project" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 19039, 19043, 21990, 22005, 50512, 51186, 51187, 53096, 54871, 54872, 54873, 54874, 62032, 62033, 62034, 62035, 62036, 62037 ], "vocabularyKeywords": [], "identifier_set": [ 10703 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130407, 130408, 130409, 130410, 130411, 130412, 130414, 130413, 130415, 168907, 130416, 130417, 130418, 130419 ], "onlineresource_set": [] }, { "ob_id": 30188, "uuid": "549bce914f66451d838bff5c0a557b37", "title": "WCRP CCMI-1: UM-UKCA output following the CCMI refC1SD experiment protocol", "abstract": "This dataset contains model output for refC1SD experiment using version 7.3 of the Met Office Unified Model, based on the science version HadGEM3-A configuration (Hewitt et al.,2011, DOI:10.5194/gmd-4-223-2011) coupled with the United Kingdom Chemistry and Aerosol scheme. The simulation follows the experimental design of the IGAC/Stratosphere troposphere Processes And their Role in Climate (SPARC) Chemistry-Climate Model Initiative (CCMI) refC1SD experiment. This simulation spans 1979-2010, with emissions taken from MACCity (Granier et al., 2011, DOI:10.1007/s10584-011-0154-1), and uses prescribed sea surface temperatures and sea ice from HadISST (Rayner et al., 2003, DOI:10.1029/2002JD002670). Horizontal wind components (u and v) and potential temperature (θ) are nudged (following (Telford et al., 2008, DOI:10.5194/acp-8-1701-2008) to ERA-Interim Reanalysis data (Dee et al.,2011, DOI:10.1002/qj.828). \r\n\r\nThe data comprise monthly mean output over the period 1979-2010 for ozone, ozone production and loss, and diagnostics suitable for the calculation of the tropospheric ozone budget. We archive data from this experiment as HIST, and iIn order to assess the role of recent changes to the stratospheric ozone burden on the tropospheric ozone budget, a second integration (fODS_LBC) is archived in which the lower boundary condition of all halogenated ODS was kept constant at 1979 values, but all other forcings and emissions were allowed to evolve as in the HIST experiment.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-13T14:24:07", "updateFrequency": "notPlanned", "dataLineage": "Data were generated using the Met Office Unified Model (UM) at N48 horizontal resolution over global domain. The data were sent by the data provider to the Centre for Environmental Data Analysis (CEDA) for archiving.", "removedDataReason": "", "keywords": "IGAC, SPARC, CCMI-1, refC1SD, ozone, model, climate", "publicationState": "citable", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "completed", "dataPublishedTime": "2020-03-23T11:12:51", "doiPublishedTime": "2020-03-23T14:30:48", "removedDataTime": null, "geographicExtent": { "ob_id": 2608, "bboxName": "", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30189, "dataPath": "/badc/deposited2020/methane-chemistry-climate-model/data/UKCA_StratTrop_REFC1SD_FODS", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 43798174508, "numberOfFiles": 14, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8211, "startTime": "1979-01-01T00:00:00", "endTime": "2010-01-01T23:59:59" }, "resultQuality": { "ob_id": 3395, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA)", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-03-13" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": { "ob_id": 30187, "uuid": "00e79957023446628a4ec3da86baaa7b", "short_code": "comp", "title": "Met Office operational unified model (UM) deployed on unknown computer", "abstract": "Met Office operational unified model (UM) deployed on unknown computer" }, "procedureCompositeProcess": null, "imageDetails": [ 146 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30186, "uuid": "80f56196c4184a77a581b26591d7348b", "short_code": "proj", "title": "ACCI- Atmospheric Chemistry-Climate Interactions", "abstract": "ACCI was a ERC funded project" } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 6023, 19039, 19043, 21990, 22005, 51186, 51187, 54871, 54872, 54873, 54874, 62034, 62037, 62039, 62040, 62041, 62042, 62043, 62044, 62045, 62046, 62047, 62048, 62049, 62050, 62051, 62052, 62053, 62054, 62055, 62056, 62353, 66241 ], "vocabularyKeywords": [], "identifier_set": [ 10702 ], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130420, 130421, 130422, 130423, 130424, 130425, 130428, 130427, 130430, 168805, 130432, 130433, 130429, 130431 ], "onlineresource_set": [ 87550 ] }, { "ob_id": 30193, "uuid": "05cea0662aa54aa2b7e2c5811e09431f", "title": "Defra and JNCC Sentinel-1 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-1 data processed into a normalised radar backscatter product on a linear scale in dB. Products acquired from ESA are Ground-Range Detected (GRD) Interferometric Wide-swath (IW) in the dual VV+VH polarisation (DV) mode, where both VV and VH polarisations are collected. Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nSentinel-1 scenes processed before July 2021 have had a strip of data clipped from their northern edge to remove an artefact caused by a deprecated processing method. Details can be found in the lineage statement of the metadata for all affected scenes.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-18T16:01:59", "updateFrequency": "daily", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Analysis Ready Data, Sentinel, Backscatter, Synthetic Aperture Radar, ARD", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-03-30T10:59:05", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 30194, "dataPath": "/neodc/sentinel_ard/data/sentinel_1/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 141781574024939, "numberOfFiles": 72080, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format. Each product comes with xml metadata file." }, "timePeriod": { "ob_id": 9028, "startTime": "2018-01-01T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3396, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-03-23" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 30196, "uuid": "343e4686a1ac4108b60a9888c9983364", "short_code": "cmppr", "title": "DEFRA and JNCC Sentinel-1 Analysis Ready Data", "abstract": "Sentinel-1 GRD products processed into a backscatter product using ESA SNAP. Processing included the following steps: apply orbit file, border and thermal noise removal, radiometric calibration, multilook, terrain correction using the Aerial Photography Great Britain (APGB) 10m resolution DEM, radiometric normalisation (Kellndorfer approach, Kellndorfer et al., 1998) and refined Lee speckle filter. Data is outputted on a linear scale in dB. A final reprojection is applied to transform the data to OSGB1936 (EPSG:27700) using GDAL. Please see individual image metadata for the versions of software used in processing." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130437, 130438, 130440, 130445, 130441, 130444, 130443, 130442, 130439 ], "onlineresource_set": [ 36998, 36999, 36990, 41814, 92976 ] }, { "ob_id": 30199, "uuid": "bf9568b558204b81803eeebcc7f529ef", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data (ARD)", "abstract": "These data have been created by the Department for Environment, Food and Rural Affairs (Defra) and Joint Nature Conservation Committee (JNCC) in order to cost-effectively provide high quality, Analysis Ready Data (ARD) for a wide range of applications. The dataset contains modified Copernicus Sentinel-2 (Level 1C data processed into a surface reflectance product (Level 2)). Defra and JNCC data were processed on separate platforms using a common specification to produce complementary outputs up to and including the acquisition date 23/06/2023. Data acquired after that date were processed on a single platform to the same specification.\r\n\r\nThe majority of data captured between July 2015 and August 2017 was processed by Aberystwyth University for Defra and later updated by JNCC to the same specification as the rest of this dataset. Please see the image-level metadata for details of data lineage and processing.\r\n\r\nThe Sentinel-2 ARD filename format was changed in April 2023. Filenames of data acquired on or after 01/04/2023 include the timestamp of data generation and display image latitude and longitude to a consistent number of significant figures preceded by ‘n’ (North) and ‘e/w’ (East / West). Filenames of data acquired before this date do not include the data generation timestamp and display latitude and longitude to varying significant figures not preceded by ‘n’ and ‘e/w’.", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57", "latestDataUpdateTime": "2025-07-18T06:33:44", "updateFrequency": "", "dataLineage": "Data were produced by the project team and supplied for archiving at the Centre for Environmental Data Analysis (CEDA).", "removedDataReason": "", "keywords": "Analysis Ready Data, Sentinel, Surface Reflectance, ARD", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": false, "language": "English", "resolution": "", "status": "ongoing", "dataPublishedTime": "2020-03-30T11:16:01", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 2609, "bboxName": "UK", "eastBoundLongitude": 2.02, "westBoundLongitude": -10.85, "southBoundLatitude": 49.82, "northBoundLatitude": 59.48 }, "verticalExtent": null, "result_field": { "ob_id": 30198, "dataPath": "/neodc/sentinel_ard/data/sentinel_2/", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 90366026836654, "numberOfFiles": 528566, "fileFormat": "These data are provided in Cloud Optimized GeoTIFF (COG) format. Each product comes with valid pixel, topographic shadow, saturated pixel and cloud masks and metadata files" }, "timePeriod": { "ob_id": 9029, "startTime": "2015-07-08T00:00:00", "endTime": null }, "resultQuality": { "ob_id": 3397, "explanation": "Data are as given by the data provider, no quality control has been performed by the Centre for Environmental Data Analysis (CEDA).", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2020-03-24" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 30201, "uuid": "8cdfbba2f037485d9c562cf86355d23b", "short_code": "cmppr", "title": "Defra and JNCC Sentinel-2 Analysis Ready Data", "abstract": "Sentinel-2 Level 1C products processed into a Level 2 surface reflectance product using ARCSI (https://www.arcsi.remotesensing.info/) Processing used the following steps: reprojection to OSGB 1936/British National Grid (EPSG27700), band sharpening to 10m (removing the native 60m ESA Bands 1, 9 and 10), cloud masking, atmospheric correction and topographic correction using the Aerial Photography Great Britain (APGB) 10m resolution DEM. Please see individual image metadata for the versions of software used in processing." }, "imageDetails": [], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2526, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 3, "licenceURL": "http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 30197, "uuid": "14818d07b867480c90ed01fbf81d9346", "short_code": "proj", "title": "Defra and JNCC Analysis Ready Sentinel Data", "abstract": "Defra and JNCC aim to provide timely, cost-effective, and high-quality, Analysis Ready Sentinel Data (ARD) for a wide range of applications. These data are provided for the UK geographic area for this project. The indices datasets currently cover Yorkshire, UK, but coverage will be expanded in the future. These products have been produced using the Copernicus satellites, Sentinel 1 and Sentinel 2." }, { "ob_id": 44948, "uuid": "47150f1b97dd4dabae115c8369860771", "short_code": "proj", "title": "JNCC NCEA Habitat Condition Change Project", "abstract": "Sentinel 1 and Sentinel 2 derived index layers have been created by the Joint Nature Conservation Committee (JNCC) as part of the Earth observation-based “Habitat Condition Change” project (HabCon). This project is funded by the Department for Environment, Food and Rural Affairs (Defra) as part of the Natural Capital and Ecosystem Assessment (NCEA) programme. The project seeks to facilitate the effective uptake and use of Earth Observation data by producing data and tools for investigating and detecting parcel-level change in habitats and habitat condition.\r\nIndex files were derived from Defra and JNCC Sentinel 1 and Sentinel 2 ARD across England and Scotland for the period 2015 to 2025 and will be added to regularly. \r\nThe Sentinel 2 dataset contains NDVI, NDMI, NDWI, NBR and EVI2 indices. \r\nFor Sentinel 1, VH/VV cross-ratio files have been generated for Sentinel-1A ascending orbit granules only." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [], "vocabularyKeywords": [], "identifier_set": [], "observationcollection_set": [], "responsiblepartyinfo_set": [ 130452, 130453, 130455, 130456, 130458, 130459, 130460, 130457, 130454 ], "onlineresource_set": [ 36995, 36996, 36997, 41813, 88384 ] }, { "ob_id": 30204, "uuid": "1d38c469bbd3411b9bc4cd1195c38331", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 04.7", "abstract": "The Soil Moisture CCI ACTIVE dataset is one of the three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created. \r\n\r\nThe v04.7 ACTIVE product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It covers the period 1991-08-05 to 2019-12-31 and is expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2024-09-11T12:08:56", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the v04.7 Soil Moisture dataset (doi:10.5285/0683e320d8634a37aa1d9ef62dd41a0d) https://dx.doi.org/10.5285/0683e320d8634a37aa1d9ef62dd41a0d.", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, Active", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degrees", "status": "superseded", "dataPublishedTime": "2020-04-16T13:14:59", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30205, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/ACTIVE/v04.7", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 8361304467, "numberOfFiles": 10377, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8216, "startTime": "1991-08-05T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3251, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-02-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27120, "uuid": "972ce1b35bbe40d7b8a889d2e65f4fb0", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Active sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The active product has been created by fusing scatterometer soil moisture products, derived from the instruments AMI-WS and ASCAT." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54644, 54645, 54646, 54647, 54648, 54649 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30210, "uuid": "0683e320d8634a37aa1d9ef62dd41a0d", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.7 data collection", "abstract": "Soil Moisture data (version 04.7) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31 and the ACTIVE product covering 1991-08-05 to 2019-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 130467, 130468, 130469, 130470, 130471, 130472, 130473, 130466, 130474, 130483, 130484, 130486, 130482, 130478, 130477, 130475, 130476, 130479, 130480, 130481, 130485 ], "onlineresource_set": [ 37010, 37005, 37002, 37009, 37003, 37006, 37008, 37000, 37012, 37001, 37011, 37007, 42183 ] }, { "ob_id": 30206, "uuid": "2d4a50f390064820a9dcc2fcf7ac4b18", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 04.7", "abstract": "The Soil Moisture CCI COMBINED dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by directly merging Level 2 scatterometer and radiometer soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. \r\n\r\nThe v04.7 COMBINED product, provided as global daily images in NetCDF-4 classic file format, presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. It is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-26T16:19:01", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the v04.7 Soil Moisture dataset (doi:10.5285/0683e320d8634a37aa1d9ef62dd41a0d) https://dx.doi.org/10.5285/0683e320d8634a37aa1d9ef62dd41a0d .", "removedDataReason": "", "keywords": "ESA, Soil Moisture, CCI, COMBINED", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25", "status": "superseded", "dataPublishedTime": "2020-04-16T16:03:41", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30207, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v04.7", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 15214801652, "numberOfFiles": 15037, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8215, "startTime": "1978-11-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3252, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2019-02-07" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27141, "uuid": "c92b9dd29dd64d06b18f085bb033f311", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using combined active and passive sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The combined product uses information from both active and passive sensors." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54646, 54647, 54648, 54649, 54650, 54651 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30210, "uuid": "0683e320d8634a37aa1d9ef62dd41a0d", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.7 data collection", "abstract": "Soil Moisture data (version 04.7) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31 and the ACTIVE product covering 1991-08-05 to 2019-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070" } ], "responsiblepartyinfo_set": [ 130494, 130487, 130488, 130489, 130491, 130492, 130493, 130490, 130495, 130504, 130505, 130507, 130503, 130499, 130498, 130496, 130497, 130500, 130501, 130502, 130506 ], "onlineresource_set": [ 37014, 37015, 37017, 37013, 37018, 37019, 37022, 37023, 37024, 37025, 37021, 37016, 42180 ] }, { "ob_id": 30208, "uuid": "ccc69467a0c74adbaada8c55b970ca19", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 04.7", "abstract": "The Soil Moisture CCI PASSIVE dataset is one of three datasets created as part of the European Space Agency's (ESA) Soil Moisture Essential Climate Variable (ECV) Climate Change Initiative (CCI) project. The product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v04.7 PASSIVE product presents a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The product is provided in volumetric units [m3 m-3] and covers the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31. It consists of global daily images stored within yearly folders and are NetCDF-4 classic file formatted. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Other additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project web site or within the Product Specification Document.\r\n\r\nThe data set should be cited using all three of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070", "creationDate": "2022-07-22T09:15:57.183554", "lastUpdatedDate": "2022-07-22T09:15:57.272326", "latestDataUpdateTime": "2020-03-26T16:17:36", "updateFrequency": "notPlanned", "dataLineage": "Data were processed by the ESA CCI Soil Moisture project team and transferred to CEDA for the ESA CCI Open Data Portal Project. This dataset forms part of the v04.7 Soil Moisture dataset (doi:10.5285/0683e320d8634a37aa1d9ef62dd41a0d) https://dx.doi.org/10.5285/0683e320d8634a37aa1d9ef62dd41a0d .", "removedDataReason": "", "keywords": "ESA, CCI, PASSIVE", "publicationState": "published", "nonGeographicFlag": false, "dontHarvestFromProjects": true, "language": "English", "resolution": "0.25 degree", "status": "superseded", "dataPublishedTime": "2020-04-17T07:22:46", "doiPublishedTime": null, "removedDataTime": null, "geographicExtent": { "ob_id": 529, "bboxName": "Global (-180 to 180)", "eastBoundLongitude": 180.0, "westBoundLongitude": -180.0, "southBoundLatitude": -90.0, "northBoundLatitude": 90.0 }, "verticalExtent": null, "result_field": { "ob_id": 30209, "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/PASSIVE/v04.7", "oldDataPath": [], "storageLocation": "internal", "storageStatus": "online", "volume": 12429010540, "numberOfFiles": 15037, "fileFormat": "Data are netCDF formatted." }, "timePeriod": { "ob_id": 8217, "startTime": "1978-11-01T00:00:00", "endTime": "2019-12-31T23:59:59" }, "resultQuality": { "ob_id": 3148, "explanation": "as provided by the CCI Soil Moisture team", "passesTest": true, "resultTitle": "CEDA Data Quality Statement", "date": "2018-06-25" }, "validTimePeriod": null, "procedureAcquisition": null, "procedureComputation": null, "procedureCompositeProcess": { "ob_id": 27140, "uuid": "6a1bc8226e7749d8bcbff89ca4ae3d93", "short_code": "cmppr", "title": "ESA Soil Moisture Climate Change Initiative: Retrieval of Soil Moisture using Passive sensors for version 4.4 data.", "abstract": "The ESA Soil Moisture Climate Change Initiative is using Active and Passive Sensors to derive information on soil moisture. The passive product has been created by fusing radiometer soil moisture products, merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments." }, "imageDetails": [ 111 ], "discoveryKeywords": [ { "ob_id": 1138, "name": "NDGO0003" } ], "permissions": [ { "ob_id": 2535, "accessConstraints": null, "accessCategory": "public", "accessRoles": null, "label": "public: None group", "licence": { "ob_id": 15, "licenceURL": "https://artefacts.ceda.ac.uk/licences/specific_licences/esacci_soilmoisture_terms_and_conditions.pdf", "licenceClassifications": [ { "ob_id": 3, "classification": "any" } ] } } ], "projects": [ { "ob_id": 13332, "uuid": "c256fcfeef24460ca6eb14bf0fe09572", "short_code": "proj", "title": "ESA Soil Moisture Climate Change Initiative Project", "abstract": "The European Space Agency Soil Moisture Climate Change Initiative (Soil_Moisture_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.\r\n\r\nThe Soil Moisture CCI project was set up to :\r\n - Analyse the needs of the climate research community in terms of soil moisture data.\r\n - Adapt soil moisture satellite measurements for their use by the climate research community.\r\n - Create a long-term consistent soil moisture time series, based on active and passive data, suitable for climate change studies." } ], "inspireTheme": [], "topicCategory": [], "phenomena": [ 50512, 52664, 52665, 54641, 54643, 54646, 54647, 54648, 54649, 54650, 54651 ], "vocabularyKeywords": [ { "ob_id": 10672, "vocabService": "clipc_skos_vocab", "uri": "http://vocab.ceda.ac.uk/collection/cci/ecv/cciecv_soilMst", "resolvedTerm": "soil moisture" } ], "identifier_set": [], "observationcollection_set": [ { "ob_id": 30210, "uuid": "0683e320d8634a37aa1d9ef62dd41a0d", "short_code": "coll", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Version 04.7 data collection", "abstract": "Soil Moisture data (version 04.7) from the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. This dataset collection contains three surface soil moisture datasets, alongside ancilliary data products. The ACTIVE and PASSIVE products have been created by fusing satellite scatterometer and radiometer soil moisture products respectively. In the case of the ACTIVE product, these have been derived from the AMI-WS and ASCAT satellite instruments and for the PASSIVE product from the satellite instruments SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS. The COMBINED product is generated from the Level 2 active and passive instruments.. \r\n\r\nThe homogenized and merged products present a global coverage of surface soil moisture at a spatial resolution of 0.25 degrees. The products are provided as global daily images, in NetCDF-4 classic file format, the PASSIVE and COMBINED products covering the period (yyyy-mm-dd) 1978-11-01 to 2019-12-31 and the ACTIVE product covering 1991-08-05 to 2019-12-31. The soil moisture data for the PASSIVE and the COMBINED product are provided in volumetric units [m3 m-3], while the ACTIVE soil moisture data are expressed in percent of saturation [%]. For information regarding the theoretical and algorithmic base of the datasets, please see the Algorithm Theoretical Baseline Document (ATBD). Other additional documentation and information documentation relating to the datasets can also be found on the CCI Soil Moisture project web site or in the Product Specification Document.\r\n\r\nThe data set should be cited using the all of the following references:\r\n\r\n1. Gruber, A., Scanlon, T., van der Schalie, R., Wagner, W., and Dorigo, W. (2019). Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology, Earth Syst. Sci. Data, 11, 717–739, https://doi.org/10.5194/essd-11-717-2019\r\n\r\n2. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001\r\n\r\n3. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. 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