Related Observation Info List
Get a list of RelatedObservationInfo objects.
GET /api/v3/relatedobservationinfos/?format=api&offset=400
{ "count": 1153, "next": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=500", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/relatedobservationinfos/?format=api&limit=100&offset=300", "results": [ { "ob_id": 427, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31967, "uuid": "29c4af5986ba4b9c8a3cfc33ca8d7c85", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 29962, "uuid": "1ee56c42cf6c4ef698693e00a63795f4", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Active Layer Thickness for the Northern Hemisphere, v1.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the beta version of their Climate Research Data Package (CRDP v0). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v0 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. It covers the Northern Hemisphere (north of 30°) for the period 2003-2017." } }, { "ob_id": 428, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31966, "uuid": "6ebcb73158b14cd5a321b7c0bc6ed393", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost ground temperature for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 29964, "uuid": "9a333481e9a34c7a8f78902f77ad3fe7", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v1.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the Beta version of their Climate Research Data Package (CRDP v0). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v0 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m) for the Northern Hemisphere (north of 30°) for the period 2003-2017." } }, { "ob_id": 429, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31965, "uuid": "28e889210f884b469d7168fde4b4e54f", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 29965, "uuid": "c7590fe40d8e44169d511c70a60ccbcc", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Extent for the Northern Hemisphere, v1.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the Beta version of their Climate Research Data Package (CRDP v0). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v0 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%). It covers the Northern Hemisphere (north of 30°) for the period 2003-2017." } }, { "ob_id": 430, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26241, "uuid": "f0580e34da524770b0a5d43c033b33dc", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2", "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, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v05.2 PASSIVE 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 website.\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" }, "objectObservation": { "ob_id": 30208, "uuid": "ccc69467a0c74adbaada8c55b970ca19", "short_code": "ob", "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" } }, { "ob_id": 431, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26236, "uuid": "dd3da2570363429791b51120bdd29c02", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2", "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 v05.2 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 is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 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 website.\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" }, "objectObservation": { "ob_id": 30204, "uuid": "1d38c469bbd3411b9bc4cd1195c38331", "short_code": "ob", "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" } }, { "ob_id": 432, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26244, "uuid": "057dd6c36f0741d3b97f9eee688b7835", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2", "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, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v05.2 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 website.\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" }, "objectObservation": { "ob_id": 30206, "uuid": "2d4a50f390064820a9dcc2fcf7ac4b18", "short_code": "ob", "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" } }, { "ob_id": 433, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32016, "uuid": "4530714563c24fd2a3cf291d1db8a4b2", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.2 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.2 was added due to a correction to the land-sea mask used. More details can be found in the HadEX3 blog under 'Details/Docs' tab." }, "objectObservation": { "ob_id": 31941, "uuid": "ee378533af6243899bc93653cbd41eaa", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.1 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nIn September 2020, a user identified some issues in the DTR and TN90p (61-90) indices. These were found to have arisen from erroneous values in a few stations which were not picked up by any quality control checks. These stations were noted on the bad list and these two indices re-run, hence v3.0.1." } }, { "ob_id": 434, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32021, "uuid": "901f576dacae4e049630ab879d6fb476", "short_code": "ob", "title": "CRUTEM.5.0.0.0: Climatic Research Unit (CRU) gridded near-surface air temperature anomalies over land", "abstract": "CRUTEM (Climatic Research Unit TEMperature) is a gridded dataset of global historical near-surface air temperature anomalies over land at a monthly timescale. It is a collaborative product of the Climatic Research Unit at the University of East Anglia, the Met Office Hadley Centre and the National Centre for Atmospheric Science. CRUTEM also contributes the land air temperature station data to the global (land and ocean) temperature dataset called HadCRUT.\r\n \r\nCRUTEM5 is the fifth major version of the dataset, covering the time period from 1850, with a spatial resolution of 5° latitude by 5° longitude and a monthly-mean time resolution. The gridded temperature anomaly fields are based on a compilation of monthly-mean temperature observational records from weather stations. This compilation contains 10639 station records, but only 7983 records had the necessary coverage to be used for producing the gridded dataset. Anomalies are differences from average conditions in the 1961-1990 period. Hemispheric and global mean time series of land air temperature anomalies are also provided." }, "objectObservation": { "ob_id": 11602, "uuid": "94594949023ea41d47713153ad07d44b", "short_code": "ob", "title": "CRUTEM4.2.0.0-2013-03: Climatic Research Unit (CRU) Gridded Dataset of Global Historical Near-Surface Air TEMperature Anomalies Over Land (version 4.2.0.0 Jan. 1850 - Mar. 2013)", "abstract": "CRUTEM4 is a gridded dataset of global historical near-surface air temperature anomalies over land. \r\n\r\nThis specific version is CRUTEM4.2.0.0-2013-03, available for each month from January 1850 to March 2013, on a 5 degree grid. \r\n\r\nHemispheric and global anomaly series are provided. \r\n\r\nThe dataset is a collaborative product of the Climatic Research Unit at the University of East Anglia and the Met Office Hadley Centre. \r\n\r\nThe CRUTEM4 dataset is updated on a monthly basis; these updates are available from the institutions listed below (see Links)." } }, { "ob_id": 435, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32055, "uuid": "087d4c75ace04e59a71d95c1c44918f9", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.3 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.3 was added due to an error in how the Rx1day and Rx5day data were being handled for one of the West African data sources. More details can be found in the HadEX3 blog under 'Details/Docs' tab." }, "objectObservation": { "ob_id": 32016, "uuid": "4530714563c24fd2a3cf291d1db8a4b2", "short_code": "ob", "title": "HadEX3: Global land-surface climate extremes indices v3.0.2 (1901-2018)", "abstract": "HadEX3 is a land-surface dataset of climate extremes indices available on a 1.875 x 1.25 longitude-latitude grid. These 29 indices have been developed by the World Meteorological Organization (WMO) Expert Team on Climate Change Detection and Indices (ETCCDI). Daily precipitation, as well as maximum and minimum temperature observations, are used to calculate these indices at each station. The daily data, as well as indices, have been supplied, quality controlled and combined to make a gridded set of NetCDF files covering 1901-2018 (inclusive). \r\n\r\nSpatial coverage is determined by the number of stations present at each time point as well as the spatial correlation structure between the stations for each index. The spatial coverage is lowest at the beginning of the dataset, rising until around 1960 where it plateaus, and then declines slightly after 2010.\r\n\r\nAll indices are available as annual quantities, with a subset also available on a monthly basis. A number of the indices use a reference period to determine thresholds. For these, we provide two versions, one set using 1961-1990 and another using the more recent 1981-2010 (these reference periods have been indicated in the file name as either 'ref-6190' or 'ref-8110').\r\n\r\nVersion 3.0.2 was added due to a correction to the land-sea mask used. More details can be found in the HadEX3 blog under 'Details/Docs' tab." } }, { "ob_id": 436, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 31986, "uuid": "62866635ab074e07b93f17fbf87a2c1a", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.1", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.1 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.\r\n\r\nThe dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2018. The year 1994 is omitted as there was not enough input data for this year. The dataset is distributed in NetCDF files, and it includes 4 layers: sum of burned area, standard error, fraction of burnable area and fraction of observed area. For further information on the product and its format see the Product User Guide." }, "objectObservation": { "ob_id": 26188, "uuid": "4f377defc2454db9b2a6d032abfd0cbd", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): AVHRR-LTDR Burned Area Grid product, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (CCI) project has produced maps of global burned area derived from satellite observations. The AVHRR - LTDR Grid v1.0 product described here contains gridded data of global burned area derived from spectral information from the AVHRR (Advanced Very High Resolution Radiometer) Land Long Term Data Record (LTDR) v5 dataset produced by NASA.\r\n\r\nThe dataset provides monthly information on global burned area on a 0.25 x 0.25 degree resolution grid from 1982 to 2017. The year 1994 is omitted as there was not enough input data for this year. For further information on the product and its format see the product user guide.\r\n\r\nThis v1.0 product is released as a beta version; only the gridded version of the data is available." } }, { "ob_id": 437, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32096, "uuid": "d8021685264e43c7a0868396a5f582d0", "short_code": "ob", "title": "ECMWF ERA5: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 32095, "uuid": "3c3c845f1dfb4788a2577651cd758ee9", "short_code": "ob", "title": "ECMWF ERA5: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 438, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 32096, "uuid": "d8021685264e43c7a0868396a5f582d0", "short_code": "ob", "title": "ECMWF ERA5: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 32095, "uuid": "3c3c845f1dfb4788a2577651cd758ee9", "short_code": "ob", "title": "ECMWF ERA5: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 439, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32096, "uuid": "d8021685264e43c7a0868396a5f582d0", "short_code": "ob", "title": "ECMWF ERA5: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 32094, "uuid": "bd302093953a48359ab33e4b48324f5f", "short_code": "ob", "title": "ECMWF ERA5: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 440, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32095, "uuid": "3c3c845f1dfb4788a2577651cd758ee9", "short_code": "ob", "title": "ECMWF ERA5: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 32094, "uuid": "bd302093953a48359ab33e4b48324f5f", "short_code": "ob", "title": "ECMWF ERA5: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 441, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32094, "uuid": "bd302093953a48359ab33e4b48324f5f", "short_code": "ob", "title": "ECMWF ERA5: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 27572, "uuid": "c1145ccc4b6d4310a4fc7cce61041b63", "short_code": "ob", "title": "ECMWF ERA5: surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 442, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32095, "uuid": "3c3c845f1dfb4788a2577651cd758ee9", "short_code": "ob", "title": "ECMWF ERA5: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 27572, "uuid": "c1145ccc4b6d4310a4fc7cce61041b63", "short_code": "ob", "title": "ECMWF ERA5: surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 443, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32096, "uuid": "d8021685264e43c7a0868396a5f582d0", "short_code": "ob", "title": "ECMWF ERA5: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 27572, "uuid": "c1145ccc4b6d4310a4fc7cce61041b63", "short_code": "ob", "title": "ECMWF ERA5: surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 444, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32093, "uuid": "f809e61a61ee4eb9a64d4957c3e5bfac", "short_code": "ob", "title": "ECMWF ERA5: model level analysis parameter data", "abstract": "This dataset contains ERA5 model level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nSurface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 27572, "uuid": "c1145ccc4b6d4310a4fc7cce61041b63", "short_code": "ob", "title": "ECMWF ERA5: surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 445, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 32093, "uuid": "f809e61a61ee4eb9a64d4957c3e5bfac", "short_code": "ob", "title": "ECMWF ERA5: model level analysis parameter data", "abstract": "This dataset contains ERA5 model level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nSurface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." }, "objectObservation": { "ob_id": 27572, "uuid": "c1145ccc4b6d4310a4fc7cce61041b63", "short_code": "ob", "title": "ECMWF ERA5: surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 446, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 30245, "uuid": "bbf4d911abd4446eaa8c3ed79edb9593", "short_code": "ob", "title": "ECMWF ERA5t: surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset." } }, { "ob_id": 447, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32109, "uuid": "d90fd3f22541420ab4a0d03e8fdd92d3", "short_code": "ob", "title": "ECMWF ERA5t: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." } }, { "ob_id": 448, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32109, "uuid": "d90fd3f22541420ab4a0d03e8fdd92d3", "short_code": "ob", "title": "ECMWF ERA5t: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." } }, { "ob_id": 449, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32108, "uuid": "f2f0bc7b9d0344babea9e800d9b71535", "short_code": "ob", "title": "ECMWF ERA5t: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." } }, { "ob_id": 450, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32109, "uuid": "d90fd3f22541420ab4a0d03e8fdd92d3", "short_code": "ob", "title": "ECMWF ERA5t: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 30245, "uuid": "bbf4d911abd4446eaa8c3ed79edb9593", "short_code": "ob", "title": "ECMWF ERA5t: surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data. 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It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. 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The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32110, "uuid": "cda895d99f1d47b5b1a76aa63e73cf66", "short_code": "ob", "title": "ECMWF ERA5t: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 initial release (ERA5t) surface level analysis parameter data ensemble means (see linked dataset). ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. 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The ensemble means and spreads are calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 32108, "uuid": "f2f0bc7b9d0344babea9e800d9b71535", "short_code": "ob", "title": "ECMWF ERA5t: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data from 10 member ensemble runs. 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See linked datasets for ensemble member and spread data.\r\n\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. 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This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed and, if required, amended before the full ERA5 release. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record." }, "objectObservation": { "ob_id": 30245, "uuid": "bbf4d911abd4446eaa8c3ed79edb9593", "short_code": "ob", "title": "ECMWF ERA5t: surface level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) surface level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. 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This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nSurface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. 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For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nModel level analysis and surface forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 458, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32115, "uuid": "9266a584355b46cf9d02791256e2b457", "short_code": "ob", "title": "ECMWF ERA5.1: ensemble means of surface level analysis parameter data for 2000-2006", "abstract": "This dataset contains ERA5.1 surface level analysis parameter data ensemble means over the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). The ensemble means are calculated from the ERA5.1 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record. See linked datasets for ensemble member and spread data.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\nThe main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store." }, "objectObservation": { "ob_id": 32096, "uuid": "d8021685264e43c7a0868396a5f582d0", "short_code": "ob", "title": "ECMWF ERA5: ensemble means of surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data ensemble means (see linked dataset for spreads). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 459, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32116, "uuid": "fba43af08c49445cb9150d524d8a2072", "short_code": "ob", "title": "ECMWF ERA5.1: ensemble spreads of surface level analysis parameter data for 2000-2006", "abstract": "This dataset contains spreads for the ERA5.1 surface level analysis parameter data ensemble means (see linked dataset) over the period 2000-2006. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). The ensemble means and spreads are calculated from the ERA5.1 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1).\r\n\r\nThe main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store." }, "objectObservation": { "ob_id": 32095, "uuid": "3c3c845f1dfb4788a2577651cd758ee9", "short_code": "ob", "title": "ECMWF ERA5: ensemble spreads of surface level analysis parameter data", "abstract": "This dataset contains ensemble spreads for the ERA5 surface level analysis parameter data ensemble means (see linked dataset). ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble means and spreads are calculated from the ERA5 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 460, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32100, "uuid": "f5a674c74cdd427594b6f3793b536cd0", "short_code": "ob", "title": "HadISD: Global sub-daily, surface meteorological station data, 1931-2020, v3.1.1.2020f", "abstract": "This is version 3.1.1.2020f of Met Office Hadley Centre's Integrated Surface Database, HadISD. These data are global sub-daily surface meteorological data that extends HadISD v3.1.0.2019f to include 2020 and so spans 1931-2020.\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_19310101-20210101_v3-1-1-2020f.nc. The station codes can be found under the docs tab. The station codes file has five columns as follows: 1) station code, 2) station name 3) station latitude 4) station longitude 5) station height.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISD blog: http://hadisd.blogspot.co.uk/\r\n\r\nReferences:\r\nWhen using the dataset in a paper you must cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference) :\r\n\r\nDunn, R. J. H., (2019), HadISD version 3: monthly updates, Hadley Centre Technical Note.\r\n\r\nDunn, R. J. H., Willett, K. M., Parker, D. E., and Mitchell, L.: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geosci. Instrum. Method. Data Syst., 5, 473-491, doi:10.5194/gi-5-473-2016, 2016.\r\n\r\nDunn, R. J. H., et al. (2012), HadISD: A Quality Controlled global synoptic report database for selected variables at long-term stations from 1973-2011, Clim. Past, 8, 1649-1679, 2012, doi:10.5194/cp-8-1649-2012\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent Developments and Partnerships. Bulletin of the American Meteorological Society, 92, 704–708, doi:10.1175/2011BAMS3015.1\r\n\r\nFor a homogeneity assessment of HadISD please see this following reference\r\n\r\nDunn, R. J. H., K. M. Willett, C. P. Morice, and D. E. Parker. \"Pairwise homogeneity assessment of HadISD.\" Climate of the Past 10, no. 4 (2014): 1501-1522. doi:10.5194/cp-10-1501-2014, 2014." }, "objectObservation": { "ob_id": 30000, "uuid": "e488dccd09e1446d90978b75036475e2", "short_code": "ob", "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." } }, { "ob_id": 461, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32114, "uuid": "7539b74273e14be7b226ec09c94b9bb5", "short_code": "ob", "title": "ECMWF ERA5.1: 10 ensemble member surface level analysis parameter data for 2000-2006", "abstract": "This dataset contains ERA5.1 surface level analysis parameter data for the period 2000-2006 from 10 member ensemble runs. ERA5.1 is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project re-run for 2000-2006 to improve upon the cold bias in the lower stratosphere seen in ERA5 (see technical memorandum 859 in the linked documentation section for further details). Ensemble means and spreads are calculated from these 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble mean and ensemble spread data.\r\n\r\nThe main ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects. An initial release of ERA5 data, ERA5t, are also available upto 5 days behind the present. A limited selection of data from these runs are also available via CEDA, whilst full access is available via the Copernicus Data Store." }, "objectObservation": { "ob_id": 32094, "uuid": "bd302093953a48359ab33e4b48324f5f", "short_code": "ob", "title": "ECMWF ERA5: 10 ensemble member surface level analysis parameter data", "abstract": "This dataset contains ERA5 surface level analysis parameter data from 10 ensemble runs. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. The ensemble members were used to derive means and spread data (see linked datasets). Ensemble means and spreads were calculated from the ERA5t 10 member ensemble, run at a reduced resolution compared with the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation, for which these data have been produced to provide an uncertainty estimate. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nNote, ensemble standard deviation is often referred to as ensemble spread and is calculated as the standard deviation of the 10-members in the ensemble (i.e., including the control). It is not the sample standard deviation, and thus were calculated by dividing by 10 rather than 9 (N-1). See linked datasets for ensemble member and ensemble mean data.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 462, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32117, "uuid": "88224a922439441fa6644b4564dcd90c", "short_code": "ob", "title": "Posterior South American monthly mean surface flux of methane (2010-2018) produced using the INVICAT 4D-Var inverse model.", "abstract": "This data set consist of a single file which contains a set of optimised global surface fluxes of methane (CH4), produced through variational inverse methods using the TOMCAT chemical transport model, and the INVICAT inverse transport model. These surface fluxes are produced as monthly mean values on the (approximately) 5.6-degree horizontal model grid. The associated uncertainty for the flux from each grid cell is also included. The fluxes and uncertainties are global and cover the period Jan 2010 - Dec 2018. The emissions from fossil fuels are labelled FF_FLUX, whilst the uncertainties are labelled FF_ERROR. The emissions from natural, agricultural and biomass burning sources are labelled NAT_FLUX, whilst the uncertainties are labelled NAT_ERROR. These two sectors (fossil fuel and non-fossil fuel) are solved for separately in the inversion. Flux and uncertainty units are kg(CH4)/m2/s, and time units are days since January 1st 2010. These emissions show improved performance relative to independent observations when included in the TOMCAT model. Further details about the data can be found in Wilson et al. (2020) in the documentation section." }, "objectObservation": { "ob_id": 30054, "uuid": "18ef8247f52a4cb6a14013f8235cc1eb", "short_code": "ob", "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." } }, { "ob_id": 463, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14552, "uuid": "0fb6a635c881494ea1a22fce7718d2b2", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Full Physics Level 2 Data Product (CH4_GOS_SRFP), version 2.3.7, generated with the SRFP (RemoTeC) algorithm", "abstract": "Created as part of The European Space Agency's (ESA) GHG CCI project, the XCH4 GOS Full Physics (FP) data product is a level 2, column-averaged mole fraction (mixing ratio) of methane (CH4). The product is part of Climate Research Data Package Number 3 (CRDP#3) and is based upon data generated for the years 2009-2013. It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). By contrast to the Proxy (PR) versions of the product generated with proxy algorithms, the FP products have been produced using full physics algorithms, in this case the RemoTeC SRFP baseline algorithm.\r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. \r\n\r\nFor further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section. \r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases.\r\nTo register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12811, "uuid": "2630314f738644c9b2a6bc3194d615b7", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): GOSAT CH4 Full Physics Level 2 Data Product (CH4_GOS_SRFP), version 2.3.6, generated with the SRFP (RemoTeC) algorithm", "abstract": "Created as part of The European Space Agency's (ESA) GHG CCI project, the XCH4 GOS Full Physics (FP) data product is a level 2, column-averaged mole fraction (mixing ratio) of methane (CH4). The product is part of Climate Research Data Package Number 2 (CRDP#2) and is based upon data generated for the years 2009-2013. It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). By contrast to the Proxy (PR) versions of the product generated with proxy algorithms, the record pages for which are provided in linked documentation, the FP products have been produced using full physics algorithms, in this case the RemoTeC SRFP baseline algorithm.\r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. \r\n\r\nFor further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section. \r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases.\r\nTo register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 464, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14554, "uuid": "cca6035bb0f240ffbb035e9355f09fe1", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Proxy Level 2 Data Product (CH4_GOS_SRPR), version 2.3.7, generated with the SRPR (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS SRPR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis proxy version of the product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases.\r\nTo register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12837, "uuid": "623e56750f394a37aafafce42217e032", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): GOSAT CH4 Proxy Level 2 Data Product (CH4_GOS_SRPR), version 2.3.6, generated with the SRPR (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the XCH4 GOS SRPR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis proxy version of the product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). The link to this product's record page is provided in the documentation section. However, it is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section." } }, { "ob_id": 465, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14556, "uuid": "4774bc5719754c44add5c6f209fc25ae", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Proxy Level 2 Data Product, (CH4_GOS_OCPR), version 6.0, generated with the OCPR (UoL-PR) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS PR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis version of the proxy product (version 6.0) has been generated using the OCPR University of Leicester Full-Physics Retrieval Algorithm, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra baseline algorithm. This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.\r\n\r\nThe product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12813, "uuid": "0b1f65b7aee1462eb01c7c2c416c3454", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): GOSAT CH4 Proxy Level 2 Data Product, version 5.2 (CH4_GOS_OCPR) generated with the OCPR (UoL-PR) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the XCH4 GOS PR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis version of the proxy product has been generated using version 5.2 of the OCPR University of Leicester Full-Physics Retrieval Algorithm, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra baseline algorithm. This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm, and the link to this product's record page is provided in the documentation section. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.\r\n\r\nThe product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section." } }, { "ob_id": 466, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 25928, "uuid": "f9154243fd8744bdaf2a59c39033e659", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Column-averaged CH4 from GOSAT generated with the OCPR (UoL-PR) Proxy algorithm (CH4_GOS_OCPR), v7.0", "abstract": "This CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4.\r\n\r\nThis algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.\r\n\r\nThe product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents." }, "objectObservation": { "ob_id": 14556, "uuid": "4774bc5719754c44add5c6f209fc25ae", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CH4 Proxy Level 2 Data Product, (CH4_GOS_OCPR), version 6.0, generated with the OCPR (UoL-PR) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 GOS PR (Proxy) product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). \r\n\r\nThis version of the proxy product (version 6.0) has been generated using the OCPR University of Leicester Full-Physics Retrieval Algorithm, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra baseline algorithm. This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage.\r\n\r\nThe product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" } }, { "ob_id": 467, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14563, "uuid": "3c098ba124a347678a00b0102bab9f0a", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CO2 Level 2 Data Product (CO2_GOS_OCFP) version 6.0, generated with the OCFP (UoL-FP) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project, the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). The University of Leicester Full-Physics Retrieval Algorithm has been applied to the TANSO-FTS data, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the SRFP algorithm, is also available.\r\n\r\nThe XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12862, "uuid": "130450cdf1034235aa2a5107dc513d81", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): GOSAT CO2 Level 2 Data Product (CO2_GOS_OCFP) version 5.2, generated with the OCFP (UoL-FP) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project, the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). The University of Leicester Full-Physics Retrieval Algorithm has been applied to the TANSO-FTS data, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product has also been generated from the data using the SRFP algorithm, and the link to this product's record page is provided in the documentation section. \r\n\r\nThe XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) in the documentation section or the Algorithm Theoretical Basis Document for version 5.1 of the product (no ATBD is yet available for version 5.2)." } }, { "ob_id": 468, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14565, "uuid": "c00d02a4c7fa4fbea2d6d8ebbc3be5c0", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): GOSAT CO2 Level 2 Data Product (CO2_GOS_SRFP) version 2.3.7, generated with the SRFP (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and Climate Research Data Package Number 2 (CRDP#3), the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). In this case, the RemoTeC Full Physics (SRFP) algorithm, jointly developed at SRON and KIT, has been applied to the TANSO-FTS data. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available.\r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12864, "uuid": "61424ae2a8364db2bb9cb077d644872e", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): GOSAT CO2 Level 2 Data Product (CO2_GOS_SRFP) version 2.3.6, generated with the SRFP (RemoTeC) algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and Climate Research Data Package Number 2 (CRDP#2), the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). In this case, the RemoTeC Full Physics (SRFP) algorithm, jointly developed at SRON and KIT, has been applied to the TANSO-FTS data. A second product has also been generated from the data using the OCFP (University of Leicester Full Physics) algorithm, and the link to this product's record page can be found in the documentation section. \r\n\r\nThe data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section." } }, { "ob_id": 469, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14568, "uuid": "33cd85fdc2454d2796c64c673b9427c9", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_IMAP), version 7.1, generated with the IMAP-DOAS algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the IMAP-DOAS algorithm developed at the University of Heidelberg and SRON to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. \r\n\r\nThe data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12855, "uuid": "91a09803bd5a42aeb5d3fd530409b15e", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_IMAP), version 7.0, generated with the IMAP-DOAS algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the IMAP-DOAS algorithm developed at the University of Heidelberg and SRON to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011), a link to which is provided in linked documentation. A second product has also been generated from the SCIAMACHY data using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, and the link to this product's record page is provided in the documentation section. \r\n\r\nThe data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section." } }, { "ob_id": 470, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14575, "uuid": "0ecfa9cc4f81459bba840aead5eda6cd", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_BESD), version 02.01.01, generated with the BESD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the BESD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been produced with the Bremen Optimal Estimation DOAS (BESD) algorithm, a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information of clouds and aerosols. This is the GHG CCI baseline algorithm for deriving SCIAMACHY XCO2 data: A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage in the documentation section. \r\n\r\nFor further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12968, "uuid": "b3ae3b0d11c9481bab3c9c914a6c80aa", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_BESD), version 02.00.08, generated with the BESD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the BESD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been produced with the Bremen Optimal Estimation DOAS (BESD) algorithm, a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information of clouds and aerosols. This is the GHG CCI baseline algorithm for deriving SCIAMACHY XCO2 data: A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm, the link to this product's record page being provided in the documentation section. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage in the documentation section. \r\n\r\nFor further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section." } }, { "ob_id": 471, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14577, "uuid": "95b804971495428285e136edaa6ac066", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_WFMD), version 3.9, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the WFMD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG CCI, and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage provided in the documentation section. \r\n\r\nThe data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12868, "uuid": "ce524139de81430e840d9a33daab3385", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): SCIAMACHY CO2 Level 2 Data Product (CO2_SCI_WFMD), version 3.8, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the WFMD XCO2 SCIAMACHY product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG CCI, and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). The link to this product's record page is provided in the documentation section. It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage provided in the documentation section. \r\n\r\nThe data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section." } }, { "ob_id": 472, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14579, "uuid": "3e06538585d04d9e8c848215eedeb5a4", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): Merged CO2 Level 2 Data Product (CO2_EMMA), version 2.1, generated with the EMMA algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG), the XCO2 EMMA product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced by applying the ensemble median algorithm EMMA to level 2 data of 7 XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This is therefore a merged SCIAMACHY and GOSAT XCO2 Level 2 product, primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). This version of the product covers 4 years. \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR) in the documentation section.\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12860, "uuid": "999f83c401fe4e47a0d3393b4c25c53f", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): Merged CO2 Level 2 Data Product (CO2_EMMA), version 2.0, generated with the EMMA algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG), the XCO2 EMMA product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced by applying the ensemble median algorithm EMMA to level 2 data of 7 XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This is therefore a merged SCIAMACHY and GOSAT XCO2 Level 2 product, primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). This version of the product covers 4 years. \r\n\r\nFor further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR) in the documentation section." } }, { "ob_id": 473, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 14572, "uuid": "89a49c8e8dbb4a1bb8799589ffd39dc7", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG CCI): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_WFMD), version 3.9, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the Weighting Function Modified DOAS (WFMD) algorithm to the SCIAMACHY data, a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product is also available, which has been generated from the SCIAMACHY data using the IMAP algorithm. \r\n\r\nThe data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. \r\n\r\nFor further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section\r\n\r\nThe GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/" }, "objectObservation": { "ob_id": 12857, "uuid": "5518ef811d4c45da9474056986c78cfd", "short_code": "ob", "title": "ESA Greenhouse Gases Climate Change Initiative (GHG_cci): SCIAMACHY CH4 Level 2 Data Product (CH4_SCI_WFMD), version 3.7, generated with the WFMD algorithm", "abstract": "Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 2 (CRDP#2), the XCH4 SCI product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for methane (CH4). The product has been produced using data acquired from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. \r\n\r\nThis product has been derived by applying the Weighting Function Modified DOAS (WFMD) algorithm to the SCIAMACHY data, a least-squares method based on scaling pre-selected atmospheric vertical profiles. A second product has also been generated from the SCIAMACHY data using the IMAP algorithm, and the link to this product's record page is provided in the documentation section. \r\n\r\nThe data product is stored per day in separate NetCDF-files (NetCDF-4 classic model). The product files contain the key products and other information relevant for the use of the data e.g. the averaging kernels. Note that the results since November 2005 are considered to be of reduced quality in comparison to the earlier results because the extended-wavelength part (1590-1770 nm) of SCIAMACHY's channel 6, covering the methane 2v3 absorption band used for the methane retrieval, is subject to irreversible displacement damage induced by high energy solar protons, which occurs from time to time at individual detector pixels. Therefore several affected detector pixels had to be excluded for the time period since November 2005. \r\n\r\nFor further information on the product, including details of the WFMD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section" } }, { "ob_id": 474, "relationType": "Continues", "subjectObservation": { "ob_id": 32092, "uuid": "8177330a5f2443059b7107188c2ab3c1", "short_code": "ob", "title": "ECMWF ERA5t: model level analysis parameter data", "abstract": "This dataset contains ERA5 initial release (ERA5t) model level analysis parameter data. ERA5t is the European Centre for Medium-Range Weather Forecasts (ECWMF) ERA5 reanalysis project initial release available upto 5 days behind the present data. CEDA will maintain a 6 month rolling archive of these data with overlap to the verified ERA5 data - see linked datasets on this record. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nSurface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset." }, "objectObservation": { "ob_id": 32093, "uuid": "f809e61a61ee4eb9a64d4957c3e5bfac", "short_code": "ob", "title": "ECMWF ERA5: model level analysis parameter data", "abstract": "This dataset contains ERA5 model level analysis parameter data. ERA5 is the 5th generation reanalysis project from the European Centre for Medium-Range Weather Forecasts (ECWMF) - see linked documentation for further details. This dataset contains a limited selection of all available variables and have been converted to netCDF from the original GRIB files held on the ECMWF system. They have also been translated onto a regular latitude-longitude grid during the extraction process from the ECMWF holdings. For a fuller set of variables please see the linked Copernicus Data Store (CDS) data tool, linked to from this record.\r\n\r\nSurface level analysis and forecast data to complement this dataset are also available. Data from a 10 member ensemble, run at lower spatial and temporal resolution, were also produced to provide an uncertainty estimate for the output from the single high resolution (hourly output at 31 km grid spacing) 'HRES' realisation producing data in this dataset.\r\n\r\nThe ERA5 global atmospheric reanalysis of the covers 1979 to 2 months behind the present month. This follows on from the ERA-15, ERA-40 rand ERA-interim re-analysis projects.\r\n\r\nAn initial release of ERA5 data (ERA5t) is made roughly 5 days behind the present date. These will be subsequently reviewed ahead of being released by ECMWF as quality assured data within 3 months. CEDA holds a 6 month rolling copy of the latest ERA5t data. See related datasets linked to from this record. However, for the period 2000-2006 the initial ERA5 release was found to suffer from stratospheric temperature biases and so new runs to address this issue were performed resulting in the ERA5.1 release (see linked datasets). Note, though, that Simmons et al. 2020 (technical memo 859) report that \"ERA5.1 is very close to ERA5 in the lower and middle troposphere.\" but users of data from this period should read the technical memo 859 for further details." } }, { "ob_id": 475, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26245, "uuid": "1d28a3d5d74d4439a2be8938dfb550f8", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.3", "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 v03.3 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 the complete three references as follows:\r\n\r\n1. 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\n2. 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\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 24841, "uuid": "c4f117ba38544e8a80338b6cf1000a91", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.2", "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 v03.2 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. 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\n2. 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\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 476, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26247, "uuid": "55bff4add65d489e86c195edbae8f970", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 04.2", "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 v04.2 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. 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\n2. 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\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" }, "objectObservation": { "ob_id": 26245, "uuid": "1d28a3d5d74d4439a2be8938dfb550f8", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the \"Active\", \"Passive\" and \"Combined\" products, Version 03.3", "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 v03.3 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 the complete three references as follows:\r\n\r\n1. 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\n2. 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\r\n\r\n3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014" } }, { "ob_id": 477, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 26249, "uuid": "91719888102e4c81b7884cb57cb2f3e3", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 05.2", "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 v05.2 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" }, "objectObservation": { "ob_id": 30211, "uuid": "4ef9ebc392714b7cbc86ce601c6fd956", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 04.7", "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 v04.7 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" } }, { "ob_id": 478, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32197, "uuid": "31137897d305407c9b83d49d124e4d1d", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3", "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, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v05.3 PASSIVE 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 2020-12-31. 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 website.\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" }, "objectObservation": { "ob_id": 26241, "uuid": "f0580e34da524770b0a5d43c033b33dc", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.2", "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, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v05.2 PASSIVE 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 website.\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" } }, { "ob_id": 479, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32195, "uuid": "e43aead9947549078c2d108b2c3632b2", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3", "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, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v05.3 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 2020-12-31. 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 website.\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" }, "objectObservation": { "ob_id": 26244, "uuid": "057dd6c36f0741d3b97f9eee688b7835", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.2", "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, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v05.2 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 website.\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" } }, { "ob_id": 480, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32193, "uuid": "1da8dadcdfb642f4aad2384f02efe756", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.3", "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 v05.3 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 is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. 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 website.\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" }, "objectObservation": { "ob_id": 26236, "uuid": "dd3da2570363429791b51120bdd29c02", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.2", "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 v05.2 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 is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 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 website.\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" } }, { "ob_id": 481, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32191, "uuid": "0db7bf6c11284ca2a6177b85c875364a", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 05.3", "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 v05.3 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" }, "objectObservation": { "ob_id": 26249, "uuid": "91719888102e4c81b7884cb57cb2f3e3", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 05.2", "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 v05.2 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" } }, { "ob_id": 482, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32224, "uuid": "be40716c1036498cb6b16b0ef25c5535", "short_code": "ob", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from ATSR2 and AATSR, version 3", "abstract": "This dataset provides a version of the Cloud_cci ATSR2-AATSRv3 monthly gridded dataset in Obs4MIPs format. 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). \r\n\r\nThis dataset is based on measurements taken by the Along-Track Scanning Radiometer (ATSR-2) on-board the European Remote Sensing Satellite -2 (ERS-2), and by the Advanced Along-Track Scanning Radiometer (AATSR) on-board the Environmental Satellite (Envisat). It contains a variety of cloud properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. \r\n\r\nThis particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. \r\n\r\nIndividual files are provided covering seven cloud variables:\r\nCloud area fraction in atmospheric layer (clCCI);\r\nAtmospheric cloud ice content (clivi);\r\nCloud area fraction (cltCCI);\r\nLiquid water cloud area fraction in atmospheric layer(clwCCI);\r\nLiquid water cloud area fraction (clwtCCI);\r\nAtmosphere mass content of cloud condensed water (clwvi);\r\nAir pressure at cloud top (pctCCI)" }, "objectObservation": { "ob_id": 30055, "uuid": "326bf808aedd41fd85594fc06678d20a", "short_code": "ob", "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" } }, { "ob_id": 483, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32235, "uuid": "ff5f152a3f194ab1be33543f291e65cd", "short_code": "ob", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-AM), version 3", "abstract": "This dataset provides a version of the Cloud_cci AVHRR-AMv3 monthly gridded dataset in Obs4MIPs format. 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). \r\n\r\nThis dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime morning (AM) satellite NOAA-12,-15,-17, and the EUMETSAT Metop-A satellite. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. \r\n\r\nThis particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. \r\n\r\nIndividual files are provided covering seven cloud variables:\r\nCloud area fraction in atmospheric layer (clCCI);\r\nAtmospheric cloud ice content (clivi);\r\nCloud area fraction (cltCCI);\r\nLiquid water cloud area fraction in atmospheric layer(clwCCI);\r\nLiquid water cloud area fraction (clwtCCI);\r\nAtmosphere mass content of cloud condensed water (clwvi);\r\nAir pressure at cloud top (pctCCI)" }, "objectObservation": { "ob_id": 30059, "uuid": "fb3750f5b2544403873f8788b3ed7817", "short_code": "ob", "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." } }, { "ob_id": 484, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32237, "uuid": "919157930d7447caac6d42e84e377289", "short_code": "ob", "title": "ESA Cloud Climate Change Initiative (Cloud_cci): Obs4MIPs format monthly gridded cloud products from AVHRR (AVHRR-PM), version 3", "abstract": "This dataset provides a version of the Cloud_cci AVHRR-PMv3 monthly gridded dataset in Obs4MIPs format. 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). \r\n\r\nThis dataset is based on intercalibrated measurements from the Advanced Very High Resolution Radiometer (AVHRR) sensors on-board the NOAA prime afternoon (PM) satellite NOAA-7,-9,11,-14,-16,-18,-19 satellites. It contains a multi-annual, global dataset of cloud and radiation properties which were derived employing the Community Cloud retrieval for Climate (CC4CL) retrieval framework. \r\n\r\nThis particular Obs4MIPS product has been generated for inclusion in Obs4MIPs (Observations for Model Intercomparisons Project), which is an activity to make observational products more accessible for climate model intercomparisons. \r\n\r\nIndividual files are provided covering seven cloud variables:\r\nCloud area fraction in atmospheric layer (clCCI);\r\nAtmospheric cloud ice content (clivi);\r\nCloud area fraction (cltCCI);\r\nLiquid water cloud area fraction in atmospheric layer(clwCCI);\r\nLiquid water cloud area fraction (clwtCCI);\r\nAtmosphere mass content of cloud condensed water (clwvi);\r\nAir pressure at cloud top (pctCCI)" }, "objectObservation": { "ob_id": 30067, "uuid": "004fd44ff5124174ad3c03dd2c67d548", "short_code": "ob", "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." } }, { "ob_id": 485, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32065, "uuid": "84403d09cef3485883158f4df2989b0c", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2017 and 2018, v2", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2017 and 2018. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR instrument and JAXA’s Advanced Land Observing Satellite (ALOS-1 and ALOS-2), along with additional information from Earth observation sources. 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) (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 the standard deviation in Mg/ha (raster dataset)\r\n\r\nThis release of the data is version 2, with data provided in both netcdf and geotiff format. The quantification of AGB changes by taking the difference of two maps is strongly discouraged due to local biases and uncertainties. Version 3 maps will ensure a more realistic representation of AGB changes." }, "objectObservation": { "ob_id": 29951, "uuid": "bedc59f37c9545c981a839eb552e4084", "short_code": "ob", "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." } }, { "ob_id": 486, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32146, "uuid": "de75072edfca44bfaaec0ed171d86bde", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30592, "uuid": "88c2bc7af4f0402d8ceecad611c58cc5", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, this the IOP data is also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 487, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32145, "uuid": "e9f82908fd9c48138b31e5cfaa6d692b", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites) covering the period 1997 - 2020. Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30594, "uuid": "5400de38636d43de9808bfc0b500e863", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a geographic projection at 4 km spatial resolution and at number of time resolutions (daily, 5day, 8day and monthly composites). Note, this chlor_a data is also included in the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 488, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32144, "uuid": "e94f2810c0794175b834153a71ac3182", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30596, "uuid": "db32212d86f9431dae67076dd122565e", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 489, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32143, "uuid": "8154e881452f49c1ba86982ed88b20f0", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 5.0 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30598, "uuid": "aab98144131244f58ce1b56e7342ff3e", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a sinusoidal projection (All Products), Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 4.2 generated ocean colour products on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). \r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 490, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32142, "uuid": "e2f9d8f61a02431997361a8827eaf558", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30600, "uuid": "1f84f9465e65416ca45cd20bc415b522", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global dataset of inherent optical properties (IOP) gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 inherent optical properties (IOP) product (in mg/m3) on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the IOP data are also included in the 'All Products' dataset. \r\n\r\nThe inherent optical properties (IOP) dataset consists of the total absorption and particle backscattering coefficients, and, additionally, the fraction of detrital & dissolved organic matter absorption and phytoplankton absorption. The total absorption (units m-1), the total backscattering (m-1), the absorption by detrital and coloured dissolved organic matter, the backscattering by particulate matter, and the absorption by phytoplankton share the same spatial resolution of ~4 km. The values of IOP are reported for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm). \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 491, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32141, "uuid": "f30495d4425f46c489765a2f84dd6862", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30602, "uuid": "d6d0d7b4cf3540448b4ddcaed2f54b81", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Remote Sensing Reflectance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 492, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32140, "uuid": "66534da90ed44abebfc1b08adca4f9c3", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 5.0 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30603, "uuid": "99348189bd33459cbd597a58c30d8d10", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global chlorophyll-a data products gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains their Version 4.2 chlorophyll-a product (in mg/m3) on a sinusoidal projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Note, the chlorophyll-a data are also included in the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 493, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32139, "uuid": "e2c223cdcb4844f9a1ffe9759b61eaf4", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. It is computed from the Ocean Colour CCI Version 5.0 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30604, "uuid": "07eeca6888c645d89a7ef91de0290eca", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global attenuation coefficient for downwelling irradiance (Kd490) gridded on a sinusoidal projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Kd490 attenuation coefficient (m-1) for downwelling irradiance product on a sinusoidal projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). It is computed from the Ocean Colour CCI Version 4.2 inherent optical properties dataset at 490 nm and the solar zenith angle. Note, these data are also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a sinusoidal equal-area grid projection, matching the NASA standard level 3 binned projection. The default number of latitude rows is 4320, which results in a vertical bin cell size of approximately 4 km. The number of longitude columns varies according to the latitude, which permits the equal area property. Unlike the NASA format, where the bin cells that do not contain any data are omitted, the CCI format retains all cells and simply marks empty cells with a NetCDF fill value. (A separate dataset is also available for data on a geographic projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 494, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32138, "uuid": "016f577b631a429a8558796a74983154", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 5.0 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30588, "uuid": "aeae1a19608347f7b802691db6984343", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global ocean colour data products gridded on a geographic projection (All Products), Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains all their Version 4.2 generated ocean colour products on a geographic projection at 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Data are also available as monthly climatologies.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection.)\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 495, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32137, "uuid": "5ab5267b17254152bcdbc055747faa02", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 5.0 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites) covering the period 1997 - 2020. Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30590, "uuid": "51fc11a9438b466db2ec8bd098efe7d5", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Global remote sensing reflectance gridded on a geographic projection, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains the Version 4.2 Remote Sensing Reflectance product on a geographic projection at approximately 4 km spatial resolution and at a number of time resolutions (daily, 5-day, 8-day and monthly composites). Values for remote sensing reflectance at the sea surface are provided for the standard SeaWiFS wavelengths (412, 443, 490, 510, 555, 670nm) with pixel-by-pixel uncertainty estimates for each wavelength. These are merged products based on SeaWiFS, MERIS and Aqua-MODIS data. Note, this dataset is also contained within the 'All Products' dataset. \r\n\r\nThis data product is on a geographic grid projection, which is a direct conversion of latitude and longitude coordinates to a rectangular grid, typically a fixed multiplier of 360x180. The netCDF files follow the CF convention for this projection with a resolution of 8640x4320. (A separate dataset is also available for data on a sinusoidal projection).\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 496, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32269, "uuid": "fd0960d18dc74454b9b79e92b0628a4b", "short_code": "ob", "title": "Computed air parcel trajectory used for campaign support during the Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme.", "abstract": "Computed air parcel trajectory used for campaign support during the Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme." }, "objectObservation": { "ob_id": 3711, "uuid": "19a10a2a96d55d689a039f35d646c833", "short_code": "ob", "title": "ACSOE OXICOA EASE-96: Ground-based Atmospheric Oxidant Observations from the Mace Head Atmospheric Research Centre", "abstract": "The Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) was a 5-year Natural Environment Research Council (NERC) programme on tropospheric chemistry coordinated by the University of East Anglia and involving research groups from a number of UK universities and research institutes. The project had three consortia of UK institutes and universities, each of which focused on a different scientific topic. OXICOA (OXIdising Capacity of the Ocean Atmosphere) was a study of oxidant, radical and related gas-phase chemistry in the clean and moderately polluted marine atmosphere. The Eastern Atlantic Spring/Summer Experiments (EASE 96 and EASE 97) were carried to collect data. The dataset includes measurements of the OH and HO2 radicals, measurements of halogen oxide radicals at Mace Head in conjunction with a survey of potential organic halogen source gases. In EASE 96 the Cranfield Jetstream aircraft was deployed to measure the vertical and horizontal homogeneity of the air mass." } }, { "ob_id": 497, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32269, "uuid": "fd0960d18dc74454b9b79e92b0628a4b", "short_code": "ob", "title": "Computed air parcel trajectory used for campaign support during the Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme.", "abstract": "Computed air parcel trajectory used for campaign support during the Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) programme." }, "objectObservation": { "ob_id": 2054, "uuid": "691eb4fa46f331137da5fdf3c29d7f11", "short_code": "ob", "title": "ACSOE MAGE EAE-96: Shipborne Atmospheric Oxidants Data from on-board the RRS Challenger", "abstract": "The Atmospheric Chemistry Studies in the Oceanic Environment (ACSOE) Marine Aerosol and Gas Exchange (MAGE) Eastern Atlantic Experiment 96 (EAE-96) Shipborne Atmospheric Oxidants Data from on-board the RRS Challenger contains observations of various gases, including dimethyl sulphide DMS, and aerosols off the western coast of Ireland over June-July 1996. The data were collected to understand properties of DMS, gases and aerosols in marine boundary layer conditions." } }, { "ob_id": 498, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32297, "uuid": "c2af8764c84744de87a69db7fecf7af9", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 06.1", "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 v06.1 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 is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using 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" }, "objectObservation": { "ob_id": 32193, "uuid": "1da8dadcdfb642f4aad2384f02efe756", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE Product, Version 05.3", "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 v05.3 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 is provided in percent of saturation [%] and covers the period (yyyy-mm-dd) 1991-08-05 to 2020-12-31. 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 website.\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" } }, { "ob_id": 499, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32295, "uuid": "f5ffbd016e6b44858a33ae38ed2a149e", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 06.1", "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, SMOS, SMAP, FY-3B and GPM satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v06.1 PASSIVE 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 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using 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" }, "objectObservation": { "ob_id": 32197, "uuid": "31137897d305407c9b83d49d124e4d1d", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE Product, Version 05.3", "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, SMOS and SMAP satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v05.3 PASSIVE 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 2020-12-31. 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 website.\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" } }, { "ob_id": 500, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32296, "uuid": "43d73291472444e6b9c2d2420dbad7d6", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1", "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, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v06.1 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 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using 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" }, "objectObservation": { "ob_id": 32195, "uuid": "e43aead9947549078c2d108b2c3632b2", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED Product, Version 05.3", "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, SMOS and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v05.3 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 2020-12-31. 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 website.\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" } }, { "ob_id": 501, "relationType": "IsDerivedFrom", "subjectObservation": { "ob_id": 32310, "uuid": "3bfe0c2d51544f72837a99306a74e359", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Experimental Break-Adjusted COMBINED Product, Version 06.1", "abstract": "An experimental break-adjusted soil-moisture product has been generated by the ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci) project for the first time with their v06.1 data release. The product attempts to reduce breaks in the final CCI product by matching the statistics of the datasets between merging periods. At v06.1, the break-adjustment process (explained in Preimesberger et al. 2020) is applied only to the COMBINED product, using ERA5 soil moisture as a reference. 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, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v06.1 COMBINED break-adjusted 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 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document and Preimesberger et al. 2020. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using 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. Preimesberger, W., Scanlon, T., Su, C. -H., Gruber, A. and Dorigo, W., \"Homogenization of Structural Breaks in the Global ESA CCI Soil Moisture Multisatellite Climate Data Record,\" in IEEE Transactions on Geoscience and Remote Sensing, vol. 59, no. 4, pp. 2845-2862, April 2021, doi: 10.1109/TGRS.2020.3012896." }, "objectObservation": { "ob_id": 32296, "uuid": "43d73291472444e6b9c2d2420dbad7d6", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 06.1", "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, SMOS, SMAP, FY-3B and GPM satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v06.1 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 2020-12-31. For information regarding the theoretical and algorithmic base of the product, please see the Algorithm Theoretical Baseline Document. Additional reference documents and information relating to the dataset can also be found on the CCI Soil Moisture project website.\r\n\r\nThe data set should be cited using 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" } }, { "ob_id": 502, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32298, "uuid": "c3bd175b6ed64020b439eb08ed9c8fc2", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 06.1", "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 v06.1 Soil Moisture CCI data.\r\n\r\nThe ACTIVE, PASSIVE and COMBINED soil moisture products which these data were used to develop are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2, SMOS, SMAP, FY-3B, GPM 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 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" }, "objectObservation": { "ob_id": 32191, "uuid": "0db7bf6c11284ca2a6177b85c875364a", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 05.3", "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 v05.3 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" } }, { "ob_id": 503, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32136, "uuid": "612a615afb5d48459b385380b440b545", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 5.0", "abstract": "The ESA Ocean Colour CCI project has produced global, level 3, binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains a monthly climatology of the generated ocean colour products covering the period 1997 - 2020.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nPlease note, data from December 2020 onwards are affected by an anomaly discovered after production and resulting in a spurious jump in remote sensing reflectance. The anomaly has been corrected in the version 5.0.1 of the dataset available through the Copernicus Climate Change Service (https://doi.org/10.24381/cds.f85b319d)\r\n\r\nVersion 6.0 of this data is now also available here: https://doi.org/10.5285/5011d22aae5a4671b0cbc7d05c56c4f0" }, "objectObservation": { "ob_id": 30641, "uuid": "37e8a29d208d4a87ae4dbe1d16b2c0ef", "short_code": "ob", "title": "ESA Ocean Colour Climate Change Initiative (Ocean_Colour_cci): Monthly climatology of global ocean colour data products, Version 4.2", "abstract": "The ESA Ocean Colour CCI project has produced global level 3 binned multi-sensor time-series of satellite ocean-colour data with a particular focus for use in climate studies.\r\n\r\nThis dataset contains a monthly climatology of the generated ocean colour products.\r\n\r\nData products being produced include: phytoplankton chlorophyll-a concentration; remote-sensing reflectance at six wavelengths; total absorption and backscattering coefficients; phytoplankton absorption coefficient and absorption coefficients for dissolved and detrital material; and the diffuse attenuation coefficient for downwelling irradiance for light of wavelength 490nm. Information on uncertainties is also provided.\r\n\r\nPlease note, this dataset has been superseded. Later versions of the data are now available." } }, { "ob_id": 504, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32371, "uuid": "82b0164a4d06467ab450ff67006729c1", "short_code": "ob", "title": "HadISDH land: gridded global monthly land surface humidity data version 4.3.1.2020f", "abstract": "This is the HadISDH land 4.3.1.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2020. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the 4.2.0.2019f version to the end of 2020 and constitutes a minor update to HadISDH due to changing some of the code base from IDL and Python 2.7 to Python 3, detecting and fixing a bug in the process, and retrieving the missing April 2015 station data. These have led to small changes in regional and global average values and coverage. All other processing steps for HadISDH remain identical. Users are advised to read the update document in the Docs section for full details.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.1.2.202101p, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 30290, "uuid": "3e9f387293294f3b8a850524fcfc0c9c", "short_code": "ob", "title": "HadISDH land: gridded global monthly land surface humidity data version 4.2.0.2019f", "abstract": "This is the 4.2.0.2019f version of the HadISDH (Integrated Surface Database Humidity) land data. These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2019. \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nThis version extends the 4.1.0.2018f version to the end of 2019 and constitutes a minor update to HadISDH due to changing some of the code base from IDL to Python 3 and detecting and fixing various bugs in the process. These have led to small changes in regional and global average values and coverage. All other processing steps for HadISDH remain identical. Users are advised to read the update document in the Docs section for full details. \r\n\r\nAs in previous years, the annual scrape of NOAA’s Integrated Surface Dataset for HadISD.3.1.0.2019f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. There has been an issue with data for April 2015 whereby it is missing for most of the globe. This will hopefully be resolved by next year’s update. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\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\nWe strongly recommend that you read these papers before making use of the data, more detail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface specific humidity product for climate monitoring. Climate of the Past, 9, 657-677, doi:10.5194/cp-9-657-2013." } }, { "ob_id": 505, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32372, "uuid": "c928ef392244426b9473af92a16b0daf", "short_code": "ob", "title": "HadISDH marine: gridded global monthly ocean surface humidity data version 1.1.0.2020f", "abstract": "This is the HadISDH marine 1.1.0.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-marine s a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2020.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the 1.0.0.2019f version to the end of 2020 and constitutes a minor update to HadISDH due to change in method for calculating gridbox monthly means. All other processing steps for HadISDH remain identical. Users are advised to read the update document in the Docs section for full details.\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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775." }, "objectObservation": { "ob_id": 30531, "uuid": "ffeb0c718baf49ad845f30677944610a", "short_code": "ob", "title": "HadISDH marine: gridded global monthly ocean surface humidity data version 1.0.0.2019f", "abstract": "This is the 1.0.0.2019f version of the HadISDH (Integrated Surface Database Humidity) marine data. These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2019.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in either NetCDF or ASCII format.\r\n\r\nThis version extends the 1.0.0.2018f version to the end of 2019 and constitutes with no changes other than the addition of 2019 data. Users are advised to read the update document in the Docs section for full details. \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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I.: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data, in review, doi:XX.XXXX/essd-XX-XXXX-2020, 2020.\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to the historical marine climate record. International Journal of Climatology. doi:10.1002/joc.4775." } }, { "ob_id": 506, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32373, "uuid": "8e90b16ddd2a484897ab9737c46d6204", "short_code": "ob", "title": "HadISDH blend: gridded global monthly land and ocean surface humidity data version 1.1.1.2020f", "abstract": "This is the HadISDH blend 1.1.1.2020f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH-blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2020.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the 1.0.0.2019f version to the end of 2020. It combines HadISDH.land.4.3.1.2020f and HadISDH.marine.1.1.0.2020f and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013." }, "objectObservation": { "ob_id": 30533, "uuid": "d38d5949dfb1438185894321095583f4", "short_code": "ob", "title": "HadISDH blend: gridded global monthly ocean surface humidity data version 1.0.0.2019f", "abstract": "This is the 1.0.0.2019f version of the HadISDH (Integrated Surface Database Humidity) blend data. It combines HadISDH.land.4.2.0.2019f and HadISDH.marine.1.0.0.2019f. These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2019.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD). Data are provided in NetCDF format.\r\n\r\nThis version is the first available. \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 HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link to the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I.: Development of the HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data, in review, doi:XX.XXXX/essd-XX-XXXX-2020, 2020.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E., Jones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and temperature record for climate monitoring, Clim. Past, 10, 1983-2006, doi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E., Berry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J., Rayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to the historical marine climate record. International Journal of Climatology. doi:10.1002/joc.4775.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station data from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\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" } }, { "ob_id": 508, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32469, "uuid": "9314b7b6417049beb18570632b435e5b", "short_code": "ob", "title": "SG-WEx: Unified Model output for January 2015 over South Georgia, without island orography (run: u-ag706)", "abstract": "This dataset contains modelling output from the u-ag706 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32324, "uuid": "6cd2bd9bf3e143009a7df234e4a8f55c", "short_code": "ob", "title": "SG-WEx: Unified Model output for January 2015 over South Georgia, with island orography included (run: u-ag477)", "abstract": "This dataset contains modelling output from the u-ag477 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 510, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32527, "uuid": "6af300ef78e649e38719f1e1a53007f1", "short_code": "ob", "title": "SG-WEx: Unified Model output for June-July 2015 over South Georgia, with island orography included (run: u-ae766)", "abstract": "This dataset contains modelling output from the u-ae766 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32522, "uuid": "a945f294d03143d8b5197e65f8005915", "short_code": "ob", "title": "SG-WEx: Unified Model output for June-July 2015 over South Georgia, without island orography (run: u-af015)", "abstract": "This dataset contains modelling output from the u-af015 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 512, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32522, "uuid": "a945f294d03143d8b5197e65f8005915", "short_code": "ob", "title": "SG-WEx: Unified Model output for June-July 2015 over South Georgia, without island orography (run: u-af015)", "abstract": "This dataset contains modelling output from the u-af015 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32527, "uuid": "6af300ef78e649e38719f1e1a53007f1", "short_code": "ob", "title": "SG-WEx: Unified Model output for June-July 2015 over South Georgia, with island orography included (run: u-ae766)", "abstract": "This dataset contains modelling output from the u-ae766 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period June-July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 515, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32324, "uuid": "6cd2bd9bf3e143009a7df234e4a8f55c", "short_code": "ob", "title": "SG-WEx: Unified Model output for January 2015 over South Georgia, with island orography included (run: u-ag477)", "abstract": "This dataset contains modelling output from the u-ag477 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32469, "uuid": "9314b7b6417049beb18570632b435e5b", "short_code": "ob", "title": "SG-WEx: Unified Model output for January 2015 over South Georgia, without island orography (run: u-ag706)", "abstract": "This dataset contains modelling output from the u-ag706 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period January 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 517, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32528, "uuid": "ca00a04014ad4426abc03e952f793dde", "short_code": "ob", "title": "SG-WEx: Unified Model output for July 2015 over South Georgia, with island orography included (run: u-ab326)", "abstract": "This dataset contains modelling output from the u-ab326 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32521, "uuid": "4edcd686e1e74e2f8e4cf108eb054cc8", "short_code": "ob", "title": "SG-WEx: Unified Model output for July 2015 over South Georgia, without island orography (run: u-ab978)", "abstract": "This dataset contains modelling output from the u-ab978 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 518, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32521, "uuid": "4edcd686e1e74e2f8e4cf108eb054cc8", "short_code": "ob", "title": "SG-WEx: Unified Model output for July 2015 over South Georgia, without island orography (run: u-ab978)", "abstract": "This dataset contains modelling output from the u-ab978 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with a flat orography file for the island. See related dataset for output from a complementary run with the island's orography included for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." }, "objectObservation": { "ob_id": 32528, "uuid": "ca00a04014ad4426abc03e952f793dde", "short_code": "ob", "title": "SG-WEx: Unified Model output for July 2015 over South Georgia, with island orography included (run: u-ab326)", "abstract": "This dataset contains modelling output from the u-ab326 run of a high-resolution (1.5 km horizontal grid, 118 vertical levels up to around 75 km altitude, 30 s timestep) local-area configuration of the Met Office Unified Model run in a box over the island of South Georgia (54S, 36W), as part of the South Georgia Wave Experiment (SG-WEx) project. This run was for the time period July 2015 with the island orography included. See related dataset for output from a complementary run with a flat orography file for the island for the same time period. These were part of a group of 6 model runs for the SG-WEx project.\r\n\r\nThe aim of the modelling runs was to examine gravity wave generation and deep vertical propagation over this mountainous island. Three model time periods are archived within the SG-WEx dataset collection: January 2015, June 2015 and July 2015, each containing two runs, one including the island's orography and one without. Initial and boundary conditions are supplied by a global forecast to ensure that conditions over the island remain realistic. Meteorological fields such as wind, temperature, pressure etc were outputted and saved in hourly steps. These runs also coincided with radiosonde campaigns launched from the island.\r\n\r\nTechnical details regarding the configuration of these runs is described Vosper (2015, doi:10.1002/qj.2566). Further information and science results can be found in Jackson et al. (2018, doi:10.1175/BAMS-D-16-0151.1) and Hindley (2021, doi:10.5194/acp-21-7695-2021). See online resources linked to this record for further details." } }, { "ob_id": 519, "relationType": "IsSupplementTo", "subjectObservation": { "ob_id": 32582, "uuid": "f75c4b7739f34e02ae1a52b793c3b839", "short_code": "ob", "title": "University of Liverpool Botanical Gardens (Ness) Long-Term Monitoring: Automatic Weather Station 5 minute surface observations (2010 onwards)", "abstract": "This dataset contains 5 minute surface meteorological observations from an automatic weather station (AWS) deployed at the University of Liverpool's Ness Botanical Gardens in Cheshire, UK from 2010 onwards.\r\n\r\nThese data complement data from this site originally collected by the Met Office until 0900 UTC on 1st June 2011. Those earlier data are available within the MIDAS Open dataset collection provided under the Open Government Licence by the Met Office. Those data are also available via the Centre for Environmental Data (CEDA) Archive. This results in an overlap between the Met Office data for this site and the 5 minute AWS data within this dataset. \r\n\r\nBoth BADC-CSV and NetCDF formatted versions of the data have been provided to aid greatest usability possible of these data." }, "objectObservation": { "ob_id": 31883, "uuid": "8d85f664fc614ba0a28af3a2d7ef4533", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202007", "abstract": "The UK hourly weather observation data contain meteorological values measured on an hourly time scale. The measurements of the concrete state, wind speed and direction, cloud type and amount, visibility, and temperature were recorded by observation stations operated by the Met Office across the UK and transmitted within SYNOP, DLY3208, AWSHRLY and NCM messages. The sunshine duration measurements were transmitted in the HSUN3445 message. The data spans from 1875 to 2019.\r\n\r\nThis version supersedes the previous version of this dataset and a change log is available in the archive, and in the linked documentation for this record, detailing the differences between this version and the previous version. The change logs detail new, replaced and removed data. Of particular note, however, is that as well as including data for 2019, historical data recovery has added temperature and weather data for Bude (1937-1958), Teignmouth (1912-1930), and Eskdalemuir (1915-1948).\r\n\r\nFor details on observing practice see the message type information in the MIDAS User Guide linked from this record and relevant sections for parameter types.\r\n\r\nThis dataset is part of the Midas-open dataset collection made available by the Met Office under the UK Open Government Licence, containing only UK mainland land surface observations owned or operated by Met Office. It is a subset of the fuller, restricted Met Office Integrated Data Archive System (MIDAS) Land and Marine Surface Stations dataset, also available through the Centre for Environmental Data Analysis - see the related dataset section on this record. Note, METAR message types are not included in the Open version of this dataset. Those data may be accessed via the full MIDAS hourly weather data." } }, { "ob_id": 520, "relationType": "IsSupplementedBy", "subjectObservation": { "ob_id": 32605, "uuid": "5b22789f362c43f3b3d1c65bc30c30ee", "short_code": "ob", "title": "Deanhill C-band rain radar dual polar products", "abstract": "Dual-polar products from the Met Office's Deanhill C-band rain radar, Whiteparish, Wiltshire, England. Data include augmented ldr (linear depolarization ratio) and zdr (differential reflectivity) scan data (both long and short pulse). The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." }, "objectObservation": { "ob_id": 5739, "uuid": "18f9d05e90171ecf31e5fc8db2903852", "short_code": "ob", "title": "Deanhill C-band rain radar single polar products", "abstract": "Single-polar products from the Met Office's Deanhill C-band rain radar, Whiltshire, England. Data include reflectivity and augmented Doppler products from April 2012 and June 2008 respectively. The radar is a C-band (5.3 cm wavelength) radar and data are received by the Nimrod system at 5 minute intervals." } }, { "ob_id": 521, "relationType": "IsVariantFormOf", "subjectObservation": { "ob_id": 32702, "uuid": "12eae5e708e541f390898af4187a1c20", "short_code": "ob", "title": "Global Ocean Lagrangian Trajectories based on AVISO velocities, v2.1", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans the year 2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information.\r\n\r\nVersion 2.1 has the same resolution as version V2.0 but has double value for time variables to permit access via THREDDS" }, "objectObservation": { "ob_id": 32470, "uuid": "fe3cb5120fa74fa7974820c2e2a238a7", "short_code": "ob", "title": "Global Ocean Lagrangian Trajectories based on AVISO velocities, v2.0, 1998-2018", "abstract": "The National Centre for Earth Observation (NCEO) Long Term Science Single Centre (LTSS) Global Ocean Lagrangian Trajectories (OLTraj) provides 30-day forward and backward Lagrangian trajectories based on AVISO (Satellite Altimetry Data project) surface velocities. Each trajectory represents the path that a water mass would move along starting at a given pixel and a given day. OLTraj can be thus used to implement analyses of oceanic data in a Lagrangian framework. The purpose of OLTraj is to allow non-specialists to conduct Lagrangian analyses of surface ocean data.\r\n\r\nThe dataset has global coverage and spans 1998-2018 with a daily temporal resolution. The trajectories were generated starting from zonal and meridional model velocity fields that were integrated using the LAMTA (6-hour time step - part of ) as described in Nencioli et al., 2018 and SPASSO (Software package for and adaptive satellite-based sampling for ocean graphic cruises containing LAMTA) software user guide. Please see the documentation section below for further information." } }, { "ob_id": 522, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32600, "uuid": "7b3bddd5af4945c2ac508a6d25537f0a", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding line location for key glaciers, Antarctica, 1994-2020, v2.0", "abstract": "This dataset contains grounding line locations (GLL) for key glaciers in Antarctica, produced as part of the ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci) project. The data have been derived from satellite observations from the ERS-1/2, TerraSAR-X and Copernicus Sentinel-1 satellites, acquired between 1994 and 2020." }, "objectObservation": { "ob_id": 26543, "uuid": "bdf2cf5a78554a73bf5e57a853e3bbc0", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Grounding Line Locations for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers, Antarctica, 1995-2017, v2.0 (CCI subset)", "abstract": "Grounding line locations (GLL) data for the Ferringo, Pine Island, Thwaites, Smith, Kohler and Pope Glaciers in Antarctica, produced by the ESA Antarctic Ice Sheet Climate Change Initiative (CCI) project. The grounding lines have been derived from satellite observations from the ERS-1/2 and Copernicus Sentinel-1 instruments, acquired in the period from 1995-2017.\r\n\r\nAn extended dataset of Grounding line locations for these Glaciers is available on the ENVEO CryoPortal (http://cryoportal.enveo.at/data/)" } }, { "ob_id": 523, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32619, "uuid": "b25d4a6174de4ac78000d034f500a268", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost Ground Temperature for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 31966, "uuid": "6ebcb73158b14cd5a321b7c0bc6ed393", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost ground temperature for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost ground temperature data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures and is provided for specific depths (surface, 1m, 2m, 5m , 10m).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 524, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32614, "uuid": "6e2091cb0c8b4106921b63cd5357c97c", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 31965, "uuid": "28e889210f884b469d7168fde4b4e54f", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost extent for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost extent data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. This corresponds to average annual ground temperatures (at 2 m depth) which forms the basis for the retrieval of yearly fraction of permafrost-underlain and permafrost-free area within a pixel. A classification according to the IPA (International Permafrost Association) zonation delivers the well-known permafrost zones, distinguishing isolated (0-10%) sporadic (10-50%), discontinuous (50-90%) and continuous permafrost (90-100%).\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. \r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 525, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32612, "uuid": "67a3f8c8dc914ef99f7f08eb0d997e23", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v3.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the second version of their Climate Research Data Package (CRDP v2). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v2 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.\r\n\r\nCase A: This covers the Northern Hemisphere (north of 30°) for the period 2003-2019 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: This covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2019 using a pixel-specific statistics for each day of the year." }, "objectObservation": { "ob_id": 31967, "uuid": "29c4af5986ba4b9c8a3cfc33ca8d7c85", "short_code": "ob", "title": "ESA Permafrost Climate Change Initiative (Permafrost_cci): Permafrost active layer thickness for the Northern Hemisphere, v2.0", "abstract": "This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness.\r\n\r\nCase A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data.\r\nCase B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year." } }, { "ob_id": 526, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32602, "uuid": "e1dfd0ee655944b8a82ce0479c518747", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance basin product, v3.0, 2002-2020", "abstract": "This dataset contains the Gravimetric Mass Balance (GMB) basin product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). \r\n\r\nThe Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)\r\n\r\nThe GMB basin product provides time series of integrated mass changes for 26 drainage basins and the aggregations of the Antarctic Peninsula, East Antarctica, West Antarctica and the entire AIS. Based on the GMB basin product, ice mass balance estimates, i.e. linear trend in the change in ice mass, were derived for all drainage basins and aggregations. A gridded GMB product is also available as a separate dataset.\r\n\r\nGroh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736" }, "objectObservation": { "ob_id": 24695, "uuid": "200ff3bf37d744a48b48cb2e3565cace", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Gravimetric Mass Balance Basin products, v1.1", "abstract": "This dataset provides Gravimetric Mass Balance Basin data for the Antarctic Ice Sheet. It has been produced in the framework of the Antarctic Ice Sheets Climate Change Initiative (CCI) project, under the lead of TU Dresden. \r\n\r\nThe ice sheet mass balance, i.e. the change in ice mass over time, is determined using the US-German satellite gravimetry mission GRACE (Gravity Recovery and Climate Experiment). The Antarctic Ice Sheet CCI GMB products are based on the monthly GRACE solutions ITSG-Grace2016 by Technische Universität Graz, and comprises a time series of mass change grids covering the entire ice sheet (GMB Gridded product), along side mass change time series for different drainage basins (GMB Basin Product). \r\n\r\nThe dataset described here covers version 1.1 of the Basin product. Mass change time series are provided for a number of drainage basins. They describe the evolution of ice mass relative to a modelled reference value, defined to be the GRACE-derived mass as of 2009-01-01. Respective time series are also derived for the total areas of the West Antarctic Ice Sheet, the East Antarctic Ice Sheet, the Antarctic Peninsula and the Antarctic Ice Sheet as a whole.\r\n\r\nIf publishing results based on this dataset, please cite the following: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065.\r\n\r\nInteractive data visualisation is available at: https://data1.geo.tu-dresden.de/ais_gmb/" } }, { "ob_id": 527, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 32692, "uuid": "36dae49c76f845a18062fa96599be719", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Antarctic Ice Sheet monthly Gravimetric Mass Balance gridded product, v3.0, 2002 - 2020", "abstract": "This dataset contains the Gravimetric Mass Balance (GMB) gridded product for the Antarctic Ice Sheet (AIS), generated by TU Dresden as part of the ESA Antarctic Ice Sheet Climate Change Initiatve (Antarctic_Ice_Sheet_cci). \r\n\r\nThe Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) is based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 187 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden. (Groh & Horwath, 2021)\r\n\r\nThe GMB gridded product comprises time series of ice mass changes for cells of polar-stereographic grid with a sampling of 50x50 km² covering the entire AIS. A GMB basin product is also available as a separate dataset.\r\n\r\nGroh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736" }, "objectObservation": { "ob_id": 19879, "uuid": "2d0422ea3c4047d5829d5fbdabe0c156", "short_code": "ob", "title": "ESA Antarctic Ice Sheet Climate Change Initiative (Antarctic_Ice_Sheet_cci): Gravimetric Mass Balance Gridded product, v1.1", "abstract": "This dataset provides gridded Gravimetric Mass Balance data for the Antarctic Ice Sheet. It has been produced in the framework of the Antarctic Ice Sheets Climate Change Initiative (CCI) project, under the lead of TU Dresden. \r\n\r\nThe ice sheet mass balance, i.e. the change in ice mass over time, is determined using the US-German satellite gravimetry mission GRACE (Gravity Recovery and Climate Experiment). The Antarctic Ice Sheet CCI GMB products are based on the monthly GRACE solutions ITSG-Grace2016 by Technische Universität Graz, and comprises a time series of mass change grids covering the entire ice sheet (GMB Gridded product), along side mass change time series for different drainage basins (GMB Basin Product). \r\n\r\nThe dataset described here covers version 1.1 of the Gridded product. Time series of gridded mass changes are provided in a polar-stereographic projection (EPSG:3031) with a grid resolution of 50 km x 50 km. The gridded changes are given in millimetre of equivalent water height (mm w.eq., or kg/m2). \r\n\r\nIf publishing results based on this dataset, please cite the following: Groh, A., & Horwath, M. (2016). The method of tailored sensitivity kernels for GRACE mass change estimates. Geophysical Research Abstracts, 18, EGU2016-12065\r\n\r\nInteractive data visualisation is available at: https://data1.geo.tu-dresden.de/ais_gmb/" } }, { "ob_id": 528, "relationType": "IsNewVersionOf", "subjectObservation": { "ob_id": 29978, "uuid": "47c32530265d4d6e8fdb6c08b2330371", "short_code": "ob", "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." }, "objectObservation": { "ob_id": 26515, "uuid": "ff725747de574f7dbb8236a9c31984e5", "short_code": "ob", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 Half Hourly 0.1 degree x 0.1 degree v5", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v5. The Integrated Multi-satellitE Retrievals for GPM (IMERG) is the unified U.S. algorithm that provides the Day-1 multi-satellite precipitation product. The precipitation estimates from the various precipitation-relevant satellite passive microwave (PMW) sensors comprising the GPM constellation are computed using the 2014 version of the Goddard Profiling Algorithm (GPROF2014), then gridded, intercalibrated to the GPM Combined Instrument product, and combined into half-hourly 10x10 km fields.\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." } }, { "ob_id": 529, "relationType": "Continues", "subjectObservation": { "ob_id": 26516, "uuid": "74b47589019e45c187fb5e81b3ad4a31", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. These level 2 OCN products provide Ocean Wind field (OWI) and Surface Radial Velocity (RVL).\r\n\r\nThe OWI component is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface, derived from IW mode. The OWI component contains a set of wind vectors for each processed Level-1 input product. The norm is the wind speed in m/s and the argument is wind direction in degrees (meteorological convention = clockwise direction from where the wind blows with respect to the North). The spatial resolution of the SAR wind speed is 1 km for IW mode.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "objectObservation": { "ob_id": 32753, "uuid": "e972cb1afd34494c94d9b22c1b66daca", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. These level 2 OCN products provide Ocean Wind field (OWI) and Surface Radial Velocity (RVL).\r\n\r\nThe OWI component is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface, derived from IW mode. The OWI component contains a set of wind vectors for each processed Level-1 input product. The norm is the wind speed in m/s and the argument is wind direction in degrees (meteorological convention = clockwise direction from where the wind blows with respect to the North). The spatial resolution of the SAR wind speed is 1 km for IW mode.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." } }, { "ob_id": 530, "relationType": "Continues", "subjectObservation": { "ob_id": 32762, "uuid": "18851d1f4454455dad76141c02ad740e", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. These level 2 OCN products provide Ocean Wind field (OWI) and Surface Radial Velocity (RVL). \r\n\r\nThe OWI component is a ground range gridded estimate of the surface wind speed and direction at 10 m above the surface, derived from IW mode. The OWI component contains a set of wind vectors for each processed Level-1 input product. The norm is the wind speed in m/s and the argument is wind direction in degrees (meteorological convention = clockwise direction from where the wind blows with respect to the North). The spatial resolution of the SAR wind speed is 1km for IW mode.\r\n\r\nThe RVL surface radial velocity component is a ground range gridded difference between the measured Level-2 Doppler grid and the Level-1 calculated geometrical Doppler. The RVL component provides continuity of the ASAR Doppler grid. The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "objectObservation": { "ob_id": 26520, "uuid": "daa38655222048b3a46a0932119f0064", "short_code": "ob", "title": "Sentinel 1B C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Ocean (OCN) Level 2 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains level-2 Interferometric Wide swath (IW) Ocean (OCN) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1B satellite. Level-2 data consists of geolocated geophysical products derived from Level-1. Sentinel 1B was launched on 25th April 2016 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. 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The RVL estimates are produced on a ground-range grid.\r\n\r\nThese data are available via CEDA to any registered CEDA user." } }, { "ob_id": 531, "relationType": "Continues", "subjectObservation": { "ob_id": 12315, "uuid": "56ba0755afc54c1ba88e1ca73dcf2df5", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v3", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. The SM mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nStripmap SLCs contain one image per polarisation band from one of six overlapping beams. Each beam covers 80.1 km, covering a combined range of 375 km. Pixel spacing is determined, in azimuth by the pulse repetition frequency (PRF), and in range by the radar range sampling frequency, providing natural pixel spacing.\r\n\r\nThese data are available via CEDA to any registered user." }, "objectObservation": { "ob_id": 32775, "uuid": "6ae95449b899409790e64e23120b48e8", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): SM mode SLC Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains Stripmap Mode (SM) C-band Synthetic Aperture Radar (SAR) Single Look Complex (SLC) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The SM mode is used only on special request for extraordinary events such as emergency management. 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The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\nThese data are available via CEDA to any registered CEDA user." }, "objectObservation": { "ob_id": 12325, "uuid": "f7014a8d35b648a5983a681fa346d8fc", "short_code": "ob", "title": "Sentinel 1A C-band Synthetic Aperture Radar (SAR): Interferometric Wide (IW) mode Single Look Complex (SLC) Level 1 data, Instrument Processing Facility (IPF) v2", "abstract": "This dataset contains level 1 Interferometric Wide swath (IW) Single Look Complex (SLC) C-band Synthetic Aperture Radar (SAR) data from the European Space Agency (ESA) Sentinel 1A satellite. Sentinel 1A was launched on 3rd April 2014 and provides continuous all-weather, day and night imaging radar data. The IW mode is the main operational mode. The IW mode supports single (HH or VV) and dual (HH+HV or VV+VH) polarisation. \r\n\r\nThe IW SLC product contains one image per sub-swath, per polarisation channel, for a total of three or six images. Each sub-swath image consists of a series of bursts, where each burst was processed as a separate SLC image. The individually focused complex burst images are included, in azimuth-time order, into a single sub-swath image, with black-fill demarcation in between\r\n\r\nUnlike SM and WV SLC products, which are sampled at the natural pixel spacing, the images for all bursts in all sub-swaths of an IW SLC product are re-sampled to a common pixel spacing grid in range and azimuth. The resampling to a common grid eliminates the need for further interpolation in case, in later processing stages, the bursts are merged to create a contiguous ground range, detected image.\r\n\r\nThese data are available via CEDA to any registered CEDA user." } } ] }