Result List
Get a list of Result objects. Results have a 1:1 mapping with Observations.
GET /api/v3/results/?format=api&offset=10800
{ "count": 11555, "next": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=10900", "previous": "https://api.catalogue.ceda.ac.uk/api/v3/results/?format=api&limit=100&offset=10700", "results": [ { "ob_id": 42876, "uuid": "f04e2d0a4b704a309d7a65d832363035", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20100414-20110525", "numberOfFiles": 335, "volume": 13213989, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 5427, "uuid": "04f0c7cfdea6316df21bfdcd08a2073b", "short_code": "ob", "title": "Vertical wind profile data from 14th April 2010 to 25th May 2011 measured by the University of Manchester 1290 mhz mobile wind profiler deployed on long term observations at Met Office Research Unit, Cardington, Bedfordshire", "abstract": "Vertical profiles of horizontal and vertical wind components as well as signal-to-noise (SNR) and spectal width measurements were collected at the Met Office Research Unit, Cardington, Bedfordshire, UK, between 14th April 2010 and 25th May 2011 as part of ongoing long term observations made by the NERC National Centre for Atmospheric Science (NCAS). These data were collected by the NCAS Atmospheric Measreument Facility's (AMF) 1290 MHz Mobile Wind Profiler, owned and operated by the University of Manchester and previously known as the aber-radar-1290mhz at the time of these observations. The data are available at 15 minute intervals as netCDF files to all registered BADC users under the Open Government License.\r\n\r\nThe dataset contains the following measurements:\r\n\r\nEastward wind velocity component\r\nNorthward wind velocity component\r\nUpward air velocity\r\nDirection the wind is from\r\nSignal to noise ratio\r\nAltitude of instrument above the ground\r\nLongitude of instrument\r\nLatitude of instrument\r\nSpectral width" }, "onlineresource_set": [] }, { "ob_id": 42877, "uuid": "3c13161e9de0490ebddf754d83be7b57", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-sodar-1/20191016_mosaic/v1.0/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42878, "uuid": "591fe53b90724f47a8088aa4fb64dbb2", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v3.0/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42879, "uuid": "97b1913063c64e59808ce4b5d2b07c25", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/ncas-mobile-xband-radar/20161101-20180604", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42880, "uuid": "8c54951bb7a44988938e71ab5adfd4b1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20040415-20040419", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42881, "uuid": "fe9637d3e49742fe84f15bd0d8cfb043", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20070328-20070516", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42882, "uuid": "5624465699074d9da9ac17dc0be7ed5f", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v2.0", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42883, "uuid": "63c8768addbc4c3a84a052449eaf49cf", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20131106-20160118", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42884, "uuid": "6498c0f6601f478c824f1e7872b70462", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-lidar-dop-1/20191006_mosaic/v1.0", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42885, "uuid": "2012021030e143de8267bd607080c4a5", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-long-term/data/man-radar-1290mhz/20170216-20170410/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42886, "uuid": "9e3aae6c3d7544f181cb94b53c27470c", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20120307-20120627", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42887, "uuid": "55a4fc93c84049b698817add57e924a7", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-mobile-x-band-radar-1/20181025_raine/v1.0", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42888, "uuid": "8b95b80ad0284428a4d63224ed6dd9e1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20110719-20110912", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42889, "uuid": "00fe6ebfb6074b9b8b3171eee89b54d4", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20171017-present", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42890, "uuid": "c426818bf747418c9b68d42b1f63f940", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20080218-20080428", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42891, "uuid": "5260eadcfc7944b8afe94b1f75558853", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20080519-20090130", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42892, "uuid": "14be92140d4a448586e13938015bf82e", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20090213-20090804", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42893, "uuid": "9399c82de335406b8cc4a9c0f7c920f4", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v1.0/", "numberOfFiles": 0, "volume": 0, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 42894, "uuid": "1bfbf48fc9704a1ab2b326be5095fa75", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-cam-11/20220621_dcmex/v1.0", "numberOfFiles": 10093, "volume": 56752573539, "fileFormat": "Data are JPG formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39197, "uuid": "b839ae53abf94e23b0f61560349ccda1", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 11, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12) captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "onlineresource_set": [] }, { "ob_id": 42895, "uuid": "2f3a8aa23bf1410c9472ea54adf144d6", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-cam-12/20220621_dcmex/v1.0", "numberOfFiles": 10236, "volume": 54574269884, "fileFormat": "Data are JPG formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40991, "uuid": "d1c61edc4f554ee09ad370f6b52f82ce", "short_code": "ob", "title": "DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022", "abstract": "This dataset contains cloud images from the NCAS Camera 12, one of two identical cameras (designated as ncas-cam-11 and ncas-cam-12), captured at various sites around the Magdalena Mountains, New Mexico, USA, as part of the Deep Convective Microphysics Experiment (DCMEX). DCMEX examined the formation and development of clouds over mountains during July and August 2022.\r\n\r\nThese cameras were designed to take simultaneous images of the same object while placed a distance apart to create a stereo image, but this was not always possible; on some days only one camera was used or the two cameras were deployed in separate locations.\r\n\r\nThe images from this camera were taken during the duration of the DCMEX campaign of clouds from a range of sites. These are accompanied by similar images from a sibling camera (see connected dataset). Where the two cameras were operated at the same site they were synchronised in terms of camera settings (exposure, etc) and camera pointing directions to facilitate the onward use of images as stereoscopic imagery. For those latter instances files have been marked with stereo-a or stereo-b within the filename to denote where the images form the left of right image for such images. Other images do not contain these additional filename fields to denote when the cameras were used in stand-along mode. Note, due to the nature of coordinating images between the two cameras one was designated as the primary camera from which the settings were then conveyed to the secondary camera by the coordinating software. As a result exact image synchronisation wasn't possible and thus the secondary camera image may have a timestamp that is a second or so later." }, "onlineresource_set": [] }, { "ob_id": 42896, "uuid": "d339575da3f84d5b8f4193ab99b50ca4", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-mobile/data/ncas-opc-1/20220715_dcmex/v1.0", "numberOfFiles": 27, "volume": 144628104, "fileFormat": "Data are netCDF formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41437, "uuid": "77a0e1e3bcbb49a5b4c89fe9cc90a788", "short_code": "ob", "title": "DCMEX: aerosol number-size distribution data from Langmuir ground station for the New Mexico field campaign 2022 - Version 1.0", "abstract": "Aerosol number-size distribution data collected by the University of Manchester at the Kiva-2 site at Langmuir ground station in the Magdalena Mountains, New Mexico, between July and August 2022 as part of the Deep Convective Microphysics Experiment (DCMEX) project. Instrument supplied by the National Centre for Atmospheric Science - Atmospheric Measurement and Observation Facility (NCAS-AMOF).\r\n\r\nThis version 1.0 dataset contains the aerosol size distribution data from the Kiva-2 site at Langmuir Laboratory. A GRIMM Optical Particle Counter (OPC) model 1.108 was installed at the Langmuir Laboratory Kiva-2 site (33.97495N, 107.18100W, ~3255 m). The GRIMM OPC was supported by AMOF and the University of Manchester scientists." }, "onlineresource_set": [] }, { "ob_id": 42897, "uuid": "d2978db7488145449925621680cf63d8", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ncas-cao/data/ncas-cam-9/20160510_longterm/v1.0/", "numberOfFiles": 3, "volume": 4402937, "fileFormat": "Data are PNG formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40064, "uuid": "229d671fe5524580838cc452ee7bef18", "short_code": "ob", "title": "AMOF: cloud camera 2 imagery from Chilbolton, Hampshire (2016-present)", "abstract": "This dataset contains photographs taken by an all-sky camera located on the roof of the Receive Cabin (51.145168°N, -1.439750°E) at the National Centre for Atmospheric Science (NCAS) Chilbolton Atmospheric Observatory (CAO) in southern England, UK. Photos are taken at 5 minute intervals on a continuous basis in order to record general atmospheric conditions.\r\n\r\nThe camera is an AXIS M3027-PVE network camera, which is alternatively known as an AXIS 0556-001. In the frame of reference of the photos, the angular field of view is 187° in the horizontal and 168° in the vertical, i.e. essentially covering a hemisphere. The centre of the field of view is nominally directed towards the zenith, although it is not known with what level of accuracy. The azimuthal alignment (measured in degrees from North), ϕ_AZIMUTH, of each part of the photo can be estimated from the relationship:\r\n\r\n ϕ_AZIMUTH = 280 - ϕ_PHOTO\r\n\r\nwhere ϕ_PHOTO is the angle measured (in degrees) clockwise from the the 12 o'clock position in a polar coordinate system whose pole is at the centre of the photo. Note that ϕ_PHOTO decreases as ϕ_AZIMUTH increases. Details of how this relationship has been derived can be found in the publication available from https://doi.org/10.5281/zenodo.8096680 .\r\n\r\nThe camera synchronises its internal clock with Coordinated Universal Time (UTC). This clock is used to trigger the capture of photographs and to produce the time stamps used in the file names. For a reason that is not entirely clear, there is typically a 1 s delay between the time stamp shown in the overlay at the top of each photograph and the one used in the file name. Although the time stamp shown in the overlay is assumed to be the more appropriate one, it is not computer-readable without making use of image processing software. Consequently, the time stamp from the file name has been adopted as the official one and recorded in the embedded metadata." }, "onlineresource_set": [] }, { "ob_id": 43019, "uuid": "ba3b4e330a064fb78d47d5e689e992b0", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/pol240042", "numberOfFiles": 0, "volume": 0, "fileFormat": "NetCDF, text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43020, "uuid": "a66447dd099c4320b67c2a8f9458b27a", "short_code": "ob", "title": "Daily model outputs from coupled ocean-biogeochemistry model with passive tracers released from the location of the MV X-Press Pearl accident location 2021.", "abstract": "This dataset contains outputs from two sets of simulations from an ocean-biogeochemistry (NEMO v.4.0.4 - ERSEM) model over the Sri Lanka region. In both simulations, the model is forced with ERA5 atmosphere, and a Nucleus for European Modelling of the Ocean (NEMO) dataset (ocean only, 3 year run) as lateral ocean boundary and initial conditions. Passive tracers (urea - organic nitrogen) are released from the region of the MV X-Press Pearl accident (approximately 7.0546N, 79.7589E). Both simulations last three months each. The outputs are stored in daily averaged grid-u, grid-v, grid-w, grid-t, and tracer variable files. For one set of these simulations, 1100T of passive tracers (urea, organic nitrogen compound) are released at different rates - over 1 day, 7 days, and 15 days. The start date of the release is 25/05/2021. These simulation start and end on: 01/05/2021 - 31/07/2021. In the other set of simulations, the release rate is over 1 day, but the date of the release is shifted from the 25th May to 25th of January, 25th February, 25th July, 25th September and 25th October (2021). These simulations start and end respectively on: 01/01/2021 - 31/03/2021; 01/02/2021 - 30/04/2021; 01/07/2021-31/09/2021; 01/09/21 - 30/11/2021; 01/10/2021 - 31/12/2021. The model configuration is named SRIL34. A complementary dataset with the hourly averaged release of tracers is also public. The outputs were generated under the Climate Linked Atlantic Sector Science (CLASS) and the South Asian Nitrogen Hub (SANH; NE/S009019/1) projects." }, "onlineresource_set": [] }, { "ob_id": 43021, "uuid": "62c20c9bf185440c9dfbc18b96b9e3c5", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/pol240043", "numberOfFiles": 0, "volume": 0, "fileFormat": "NetCDF, text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43022, "uuid": "6eb1d162644f484eaff82e2e8f96fc99", "short_code": "ob", "title": "Hourly model outputs from coupled ocean-biogeochemistry model with passive tracers released from the location of the MV X-Press Pearl accident location 2021.", "abstract": "This dataset contains outputs from two sets of simulations from an ocean-biogeochemistry (NEMO v.4.0.4 - ERSEM) model over the Sri Lanka region. In both simulations, the model is forced with ERA5 atmosphere, and a Nucleus for European Modelling of the Ocean (NEMO) dataset (ocean only, 3 year run) as lateral ocean boundary and initial conditions. Passive tracers (urea - organic nitrogen) are released from the region of the MV X-Press Pearl accident (approximately 7.0546N, 79.7589E). The outputs are stored in monthly files on grid-u, grid-v, grid-w, grid-t, and tracer variables containing hourly averages. For these simulations, 1100T of passive tracers (urea, organic nitrogen compound) are released at different rates - over 1 hour and over 6 hours. The start date of the release is 25th of May 2021. These simulation start and end on: 01/05/2021 - 30/06/2021. A complementary dataset with daily release of tracers is also public. The model configuration is named SRIL34. The outputs were generated under the Climate Linked Atlantic Sector Science (CLASS) and the South Asian Nitrogen Hub (SANH; NE/S009019/1) projects." }, "onlineresource_set": [] }, { "ob_id": 43023, "uuid": "c362dba856de4a8bb800a1754f8c1ff7", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/pol240044", "numberOfFiles": 0, "volume": 0, "fileFormat": "NetCDF, text", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43024, "uuid": "074f29828a0447aaabc813e5cfeaa34f", "short_code": "ob", "title": "SRIL34 model outputs of ocean physics variables over the Sri Lanka region (2019-2021).", "abstract": "This dataset contains outputs from a three year ocean (Nucleus for European Modelling of the Ocean (NEMO)) model simulation over the Sri Lanka region, from 2019 to 2021. The model is at 1/60 degree resolution, based on General Bathymetric Chart of the Oceans (GEBCO) bathymetry. It is forced with Copernicus Marine Environment Monitoring Service (CMEMS), GLOBAL_MULTIYEAR_PHY_001_030-TDS, derived ocean boundary conditions and ERA5 atmosphere. The data are daily averages saved in monthly grid-t, grid-u, and grid-v files. The model configuration is named SRIL34, and was part of the X-Press Pearl Disaster Nitrogen Modelling project. The outputs were generated under the Climate Linked Atlantic Sector Science (CLASS) and the South Asian Nitrogen Hub (SANH; NE/S009019/1) projects." }, "onlineresource_set": [] }, { "ob_id": 43025, "uuid": "986e3c02ab424808b7bf0640f7b6dede", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/nfe240101", "numberOfFiles": 10847, "volume": 2633969823121, "fileFormat": null, "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43026, "uuid": "8d7f2727efee45d88544608e2c7bf20d", "short_code": "ob", "title": "Idealised outputs on effects of tides and waves on delta development using the model Delft3D on a 36 and 72 year scale.", "abstract": "This dataset includes model outputs of velocities, depths, sediment concentrations, and wave conditions, amongst others. The simulations model an idealised coast (19.75 km alongshore by 9 km cross-shore) and river (width 250 m by length 50 km) with constant discharge of water (1280 m3 s-1) and sediment (128 kg s-1), and with various combinations of significant wave height (up to 2 m) and tidal range (up to 6 m). They represent either 36 or 72 years of morphological development, starting from January 1, 2022. The simulations were generated using the open source model Delft3D. The data outputs were designed to explore the potential limiting effects of wave height and tidal range on delta formation. The results demonstrate that large enough combinations of these processes may prevent deltas from forming. Also included in the dataset are input files necessary to run the model code, and animations showing various processes over time in addition to bathymetry. The outputs were generated under the Natural Environment Research Council (NERC) Envision Doctoral Training Partnership (grant number: NE/S007423/1)." }, "onlineresource_set": [] }, { "ob_id": 43045, "uuid": "44369092103b4de9bc89c6a5bd9ca676", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/scfv/MODIS/v3.0", "numberOfFiles": 0, "volume": 0, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 43046, "uuid": "cf0269abd5ab43479273c43d6e953dd1", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/scfv/MODIS/v3.0", "numberOfFiles": 8278, "volume": 864551742525, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40355, "uuid": "e955813b0e1a4eb7af971f923010b4a3", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable snow (SCFV) from MODIS (2000 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from the MODIS satellite instruments, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over all land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFV time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFV product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the Snow_cci SCFV product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFV product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the Snow_cci SCFV retrieval method is applied. \r\n\r\nThe main differences of the Snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the adaptation of the retrieval method using of a spatially variable ground reflectance instead of global constant values for snow free land, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data to assure in forested areas consistency of the SCFV and the SCFG CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/80567d38de3f4b038ee6e6e53ed1af8a) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFV product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFV CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/ebe625b6f77945a68bda0ab7c78dd76b/) the following improvements were applied for the generation of the SCFV CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n•\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nENVEO is responsible for the SCFV product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFV products are available but have data gaps." }, "onlineresource_set": [] }, { "ob_id": 43047, "uuid": "2d5c15d742ed4f5697ac9f4a99763818", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/scfg/MODIS/v3.0", "numberOfFiles": 8278, "volume": 861587059895, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40354, "uuid": "80567d38de3f4b038ee6e6e53ed1af8a", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from MODIS (2000-2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from MODIS, produced by the Snow project of the ESA Climate Change Initiative programme.\r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space on land surfaces, in forested areas corrected for the masking effect of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 1 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets and permanent snow and ice areas. The coastal zones of Greenland are included. \r\n\r\nThe SCFG time series provides daily products for the period 2000 – 2022. \r\n\r\nThe SCFG product is based on Moderate resolution Imaging Spectroradiometer (MODIS) data on-board the Terra satellite. \r\n\r\nThe retrieval method of the snow_cci SCFG product from MODIS data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module developed by ENVEO (ENVironmental Earth Observation IT GmbH). For the SCFG product generation from MODIS, multiple reflective and emissive spectral bands are used. In a first step, clouds are masked using an adapted version of the Simple Cloud Detection Algorithm version 2.0 (SCDA2.0) (Metsämäki et al., 2015). All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.55 µm and 1.6 µm, and an emissive band centred at about 11 µm. The Snow_cci snow cover mapping algorithm is a two-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. \r\n\r\nThe main differences of the snow_cci snow cover mapping algorithm compared to the GlobSnow algorithm described in Metsämäki et al. (2015) are (i) improvements of the cloud screening approach applicable on a global scale, (ii) the pre-classification of snow free areas on global land areas, (iii) the usage of spatially variable background reflectance and forest reflectance maps instead of global constant values for snow free land and forest, (iv) the update of the constant value for wet snow based on analyses of spatially distributed reflectance time series of MODIS data, and (v) the update of the global forest canopy transmissivity based on forest density from Hansen et al. (2013) and forest type layers from Land Cover CCI (Defourny, 2019) to assure in forested areas consistency of the SCFG and the SCFV CRDP v3.0 from MODIS data (https://catalogue.ceda.ac.uk/uuid/e955813b0e1a4eb7af971f923010b4a3) using the same retrieval approach.\r\n\r\nPermanent snow and ice, and water areas are masked based on the Land Cover CCI data set of the year 2000. Both classes were separately aggregated to the pixel spacing of the SCFG product. Water areas are masked if more than 30 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map. Salt lakes are masked based on a manual delineation from MODIS data. The product uncertainty for observed land pixels is provided as unbiased root mean square error (RMSE) per pixel in the ancillary variable.\r\n\r\nCompared to the SCFG CRDP v2.0 (https://catalogue.ceda.ac.uk/uuid/8847a05eeda646a29da58b42bdf2a87c/) the following improvements were applied for the generation of the SCFG CRDP v3.0: \r\n1) the pre-classification module to identify snow free areas has been relaxed to consider more pixels for the SCFG retrieval; \r\n2) the SCFG retrieval has been improved adapting the spectral reflectance value for wet snow;\r\n3) the uncertainty estimation of the SCFG has been updated to account for the changes in the retrieval algorithm;\r\n4) salt lakes retrieved by manual delineation from Terra MODIS data are masked in the SCFG CRDP v3.0 and a new class for salt lakes is added in the coding;\r\n5) the time series, starting in February 2000, was extended from December 2020 to December 2022;\r\n6) two additional layers are provided for each daily product: \r\n•\tthe sensor zenith angle in degree per pixel;\r\n\tthe image acquisition time per pixel referring to the scanline time of the MODIS granule used for the classification of the pixel. \r\n\r\nThe SCFG product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\nENVEO is responsible for the SCFG product development and generation from MODIS data, SYKE supported the development.\r\n\r\nThere are a few days without any MODIS acquisitions in the years 2000, 2001, 2002, 2003, 2008, 2016 and 2022. On several days in the years 2000 to 2006, and on a few days in the years 2012, 2015 and 2016, the acquired MODIS data have either only limited coverage, or some of the MODIS data were corrupted during the download process. For these days, the SCFG products are available but have data gaps." }, "onlineresource_set": [] }, { "ob_id": 43048, "uuid": "18e1443a79e946ce9cdced1a1833e652", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/scfv/AVHRR_SINGLE/v3.0", "numberOfFiles": 40711, "volume": 616674519406, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40357, "uuid": "7491427f8c3442ce825ba5472c224322", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - viewable (SCFV) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction of viewable snow from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction viewable (SCFV) indicates the area of snow viewable from space over land surfaces. In forested areas this refers to snow viewable on top of the forest canopy. The SCFV is given in percentage (%) per pixel. \r\n\r\nThe global SCFV product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFV time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFV product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre- and post-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.630 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFV retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data set is used for product generation: ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water; permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map.\r\n\r\nThe SCFV product is aimed to serve the needs for users working in the cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFV product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation. \r\n\r\nThe SCFV AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." }, "onlineresource_set": [] }, { "ob_id": 43049, "uuid": "3e75a3b1f4cc4ba988935122fa9f775b", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/scfg/AVHRR_SINGLE/v3.0", "numberOfFiles": 40711, "volume": 613701421439, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40356, "uuid": "56ff07acabab42888afe2d20b488ec49", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Daily global Snow Cover Fraction - snow on ground (SCFG) from AVHRR (1979 - 2022), version 3.0", "abstract": "This dataset contains Daily Snow Cover Fraction (snow on ground) from AVHRR, produced by the Snow project of the ESA Climate Change Initiative programme. \r\n\r\nSnow cover fraction on ground (SCFG) indicates the area of snow observed from space over land surfaces, in forested areas corrected for the transmissivity of the forest canopy. The SCFG is given in percentage (%) per pixel. \r\n\r\nThe global SCFG product is available at about 5 km pixel size for all land areas, excluding Antarctica and Greenland ice sheets. The coastal zones of Greenland are included.\r\n\r\nThe SCFG time series provides daily products for the period 1979-2022. \r\n\r\nThe product is based on medium resolution optical satellite data from the Advanced Very High Resolution Radiometer (AVHRR). Clouds are masked using the CLARA-A3 cloud product. \r\n\r\nThe retrieval method of the snow_cci SCFG product from AVHRR data has been further developed and improved based on the ESA GlobSnow approach described by Metsämäki et al. (2015) and complemented with a pre-classification module. All cloud free pixels are then used for the snow extent mapping, using spectral bands centred at about 0.63 µm and 1.61 µm (channel 3a or the reflective part of channel 3b (ref3b)), and an emissive band centred at about 10.8 µm. The snow_cci snow cover mapping algorithm is a three-step approach: first, a strict pre-classification is applied to identify all cloud free pixels which are certainly snow free. For all remaining pixels, the snow_cci SCFG retrieval method is applied. Finally, a post-processing removes erroneous snow pixels caused either by falsely classified clouds in the tropics or by unreliable ref3b values at a global scale. \r\n\r\nThe following auxiliary data sets are used for product generation: i) ESA CCI Land Cover from 2000; water bodies and permanent snow and ice areas are masked based on this dataset. Both classes were separately aggregated to the pixel spacing of the SCF product. Water areas are masked if more than 50 percent of the pixel is classified as water, permanent snow and ice areas are masked if more than 50 percent are identified as such areas in the aggregated map; ii) Forest canopy transmissivity map; this layer is based on the tree cover classes of the ESA CCI Land Cover 2000 data set and the tree cover density map from Landsat data for the year 2000 (Hansen et al., Science, 2013, DOI: 10.1126/science.1244693). This layer is used to apply a forest canopy correction and estimate in forested areas the fractional snow cover on ground.\r\n\r\nThe SCFG product is aimed to serve the needs of users working in cryosphere and climate research and monitoring activities, including the detection of variability and trends, climate modelling and aspects of hydrology, meteorology, and biology.\r\n\r\nThe Remote Sensing Research Group of the University of Bern, in cooperation with Gamma Remote Sensing is responsible for the SCFG product development and generation. ENVEO (ENVironmental Earth Observation IT GmbH) developed and prepared all auxiliary data sets used for the product generation.\r\n\r\nThe SCFG AVHRR product comprises a few data gaps in 1979 – 1986 (1979: 22.-24.Feb.; 01.-07.Oct.; 03.-04.Nov.; 07.Nov.; 17.-18.Nov.; 1980: 22.-27.Feb.; 01.March; 03.March; 15.-20.March; 30.March – 02.April; 26.-29.June; 12.-19.July; 12.-18.Dec.; 1981: 09.-11.May; 01.-03.Aug.; 14.-23.Aug.; 1982: 28.- 31.May; 25.-26. Oct.; 1983: 27.- 31. July; 01.- 02. and 06. Aug.; 1984: 14.-15.Jan.; 06. Dec.; 1985: 01.- 24.Feb; 1986: 15. March), resulting in a 99% data coverage over the entire study period of 43 years." }, "onlineresource_set": [] }, { "ob_id": 43050, "uuid": "0cadc2c6693641999b30e2bd7e71d24d", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/swe/MERGED/v3.0", "numberOfFiles": 8899, "volume": 12968625940, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 40358, "uuid": "b06c4c5ea7694d30b33e1db04f0ecb6a", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.0", "abstract": "This dataset contains v3.0 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.\r\n\r\nSnow water equivalent (SWE) indicates the amount of accumulated snow on land surfaces, in other words the amount of water contained within the snowpack. The SWE product time series covers the period from 1979/01 to 2022/12. Northern Hemisphere SWE products are available at daily temporal resolution with alpine areas masked. \r\n\r\nThe product is based on data from the Scanning Multichannel Microwave Radiometer (SMMR) operated on National Aeronautics and Space Administration’s (NASA) Nimbus-7 satellite, the Special Sensor Microwave / Imager (SSM/I) and the Special Sensor Microwave Imager / Sounder (SSMI/S) carried onboard the Defense Meteorological Satellite Program (DMSP) 5D- and F-series satellites. The satellite bands provide spatial resolutions between 15 and 69 km. The retrieval methodology combines satellite passive microwave radiometer (PMR) measurements with ground-based synoptic weather station observations by Bayesian non-linear iterative assimilation. A background snow-depth field from re-gridded surface snow-depth observations and a passive microwave emission model are required components of the retrieval scheme.\r\n\r\nThe dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product development and generation. \r\n\r\nFor the period from 1979 to May 1987, the products are available every second day. From October 1987 till December 2022, the products are available daily. Products are only generated for the Northern Hemisphere winter seasons, usually from beginning of October till the middle of May. A limited number of SWE products are available for days in June and September; products are not available for the months July and August as there is usually no snow information reported on synoptic weather stations, which is required as input for the SWE retrieval. Because of known limitations in alpine terrain, a complex-terrain mask is applied based on the sub-grid variability in elevation determined from a high-resolution digital elevation model. All land ice and large lakes are also masked; retrievals are not produced for coastal regions of Greenland.\r\n\r\nPassive microwave radiometer data are obtained from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data-sets/) Spatially and temporally varying snow density fields are implemented into the SWE retrieval, dry snow detection algorithm has been updated and snow masking in post-production has been improved. The time series has been extended from snow_cci version 2 by two years with data from 2020 to 2022 added.\r\n\r\nThe ESA CCI phased product development framework allowed for a systematic analysis of these changes in the snow density parameterization, snow dry detection and snow masking that occurred between v2 and v3 using a series of step-wise developmental datasets. In comparison with in-situ snow courses, the correlation and RMSE of v3 improved 0.014 and 0.6 mm, respectively, relative to v2. The timing of peak snow mass is shifted two weeks later compared to v1 and reduction in peak snow mass presented in v2 is removed in v3.\r\n\r\nThis dataset has been deprecated due to data errors in the v3.0 product." }, "onlineresource_set": [] }, { "ob_id": 43051, "uuid": "9e63c66152a24b79b1861e4b0bfb148f", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_mass_flow_rate_ice_discharge/v1.0", "numberOfFiles": 8, "volume": 384604, "fileFormat": "csv, txt, png", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 39550, "uuid": "dde78ab3388a4452b43ffe3e69e91fce", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Mass flow rate ice discharge (MFID) for Greenland from CCI IV, CCI SEC, and BedMachine v1.0", "abstract": "Mass flow rate ice discharge (MFID) for Greenland ice sheet sectors. This data set is part of the ESA Greenland Ice sheet CCI project. \r\n\r\nIt provides the following CSV files: \r\n- Mass flow rate ice discharge. Units are Gt yr^{-1}.\r\n- Mass flow rate ice discharge uncertainty. Units are Gt yr^{-1}.\r\n- Coverage for each sector at each timestamp. Unitless [0 to 1].\r\n\r\nIce discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors.\r\n\r\nThe methods, including description of \"coverage\", are described in Mankoff et al. 2020." }, "onlineresource_set": [] }, { "ob_id": 43057, "uuid": "95a465e29e5d444b9d52c8f70c4eab98", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/soil_moisture/data/ancillary/v09.1", "numberOfFiles": 6, "volume": 1751588, "fileFormat": "The data are provided in netCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43056, "uuid": "7c95469ae2b7454cb389fc18ff5ce26b", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): Ancillary data used for the ACTIVE, PASSIVE and COMBINED products, Version 09.1", "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 v09.1 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 (i.e. active remote sensing) and radiometer (i.e. passive remote sensing) soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP 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\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." }, "onlineresource_set": [] }, { "ob_id": 43058, "uuid": "de4c5a70f31a49babd369253b175c070", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/ACTIVE/v09.1", "numberOfFiles": 11838, "volume": 8813892699, "fileFormat": "The data are provided in netCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41610, "uuid": "5b1caf9095d7412282f5ba6b558034e3", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): ACTIVE product, Version 09.1", "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 ACTIVE product has been created by fusing scatterometer soil moisture products, derived from the active remote sensing instruments AMI-WS and ASCAT. PASSIVE and COMBINED products have also been created.\r\n\r\nThe v09.1 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 2023-12-31. 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\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." }, "onlineresource_set": [] }, { "ob_id": 43059, "uuid": "2e93a392f43c40d7aeb505b6f2eb3089", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/PASSIVE/v09.1", "numberOfFiles": 16499, "volume": 21329380118, "fileFormat": "The data are provided in netCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41611, "uuid": "ca55ac11fc814b0d95e68a34a10539c1", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): PASSIVE product, Version 09.1", "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 PASSIVE product has been created by merging data from the SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP passive remote sensing satellite instruments. ACTIVE and COMBINED products have also been created.\r\n\r\nThe v09.1 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 2023-12-31. 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\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." }, "onlineresource_set": [] }, { "ob_id": 43060, "uuid": "4f94ab5178c54929a4229d4bba871da4", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/soil_moisture/data/daily_files/COMBINED/v09.1", "numberOfFiles": 16498, "volume": 23286634661, "fileFormat": "The data are provided in netCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41612, "uuid": "0e346e1e1e164ac99c60098848537a29", "short_code": "ob", "title": "ESA Soil Moisture Climate Change Initiative (Soil_Moisture_cci): COMBINED product, Version 09.1", "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 COMBINED product has been created by directly merging Level 2 scatterometer ('active' remote sensing) and radiometer ('passive' remote sensing) soil moisture products derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, FY-3B, FY-3C, FY3D, AMSR2, SMOS, GPM and SMAP satellite instruments. PASSIVE and ACTIVE products have also been created.\r\n\r\nThe v09.1 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 2023-12-31. 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\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." }, "onlineresource_set": [] }, { "ob_id": 43061, "uuid": "48353d757d394ac7a084c5124b38405f", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-ALTI/v1.0/", "numberOfFiles": 101, "volume": 6980385, "fileFormat": "Data are provided in both CSV and NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41458, "uuid": "44c930e1388f40728884fbdf7e28c109", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Altimetry-based River Discharge product, v1.0", "abstract": "This dataset comprises the altimetry-based river discharge (RD-ALTI) Climate Research Data Package (CRDP), derived from nadir radar altimeter missions by the ESA CCI River Discharge precursor project (RD_cci). \r\n\r\nIt provides long-term satellite river discharge (RD) time series at specified locations (defined in the \"Selection of river basins\" document, available at https://climate.esa.int/documents/2189/D2_CCI-Discharge-0004-RP_WP2_v1-1.pdf) River discharge (in m3/s) corresponds to the water volume passing through the river cross-section per unit of time. In this dataset, it is computed from a rating curve applied to long-term satellite altimeter water surface elevation (WSE) from https://catalogue.ceda.ac.uk/uuid/c5f0aa806ec444b4a4209b49efc4bb65. The rating curve is obtained by fitting the relationship between in-situ discharge and altimeter WSE with a power law following a Bayesian approach." }, "onlineresource_set": [] }, { "ob_id": 43062, "uuid": "172cf0a2fde84f44a25f5cd05c49c58e", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/Famous-Glimmer_data/", "numberOfFiles": 551, "volume": 138407167187, "fileFormat": "net-CDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43052, "uuid": "5e48b31e413b480792e4156191b654f4", "short_code": "ob", "title": "FAMOUS-Glimmer simulations with interactive North American and Greenland ice sheets (21ka and 140ka)", "abstract": "This dataset contains model inputs and outputs from ensembles of simulations and sensitivity tests performed by FAMOUS-Glimmer of the Last Glacial Maximum (LGM; 21 kilo annum before present (ka BP)) and the Penultimate Glacial Maximum (PGM; 140 ka BP), as used in the publication Patterson et al., 2024 (https://doi.org/10.5194/cp-2024-10) These data were used to help understand the difference between the North American Ice Sheet at the last two glacial maxima, explore the sensitivity of the ice sheet to uncertain model parameters and understand the role of orbit, greenhouse gases and initial conditions on the final ice sheet configurations. The output of 62 ensemble members varying climate and ice sheet model parameters for each of the LGM and PGM are included as well as the results of 8 sensitivity tests using one set of parameters but varying the initial ice sheets and climates. These simulations were created using the atmospheric general circulation model FAMOUS coupled to the Glimmer ice sheet model under LGM and PGM climate boundary conditions, including greenhouse gas concentrations and orbital parameters outlined in the PMIP4 protocols (Kageyama et al., 2017 and Menviel et al., 2019).\r\n\r\nThe model inputs include ancillary files of the prescribed sea surface temperature and sea ice fields and climate model boundary conditions, netCDF files of the ice sheet model initial condition as well as the model configuration file and updated model modifications. The outputs consist of netCDF files of monthly climate model variables from ‘Not Ruled Out Yet’ ensemble simulations, ice sheet model output from the final timestep of all ensemble simulations (including ice sheet thickness, topography, surface mass balance and velocity) and final ice sheet thickness from the sensitivity tests. Also included are excel spreadsheets of the time series of total ice volumes for all ensemble members and the list of parameter combinations used for each. The climate model data has global coverage on a 7.5x5 degree lat/lon grid. The ice sheet model data covers North America and Greenland on a Lambert Azimuthal Equal Area projection at 40x40km resolution. Each simulation was run for ~1000 climate model years, with the ice sheet model running at 10x acceleration, giving ~10,000 years of ice sheet model output." }, "onlineresource_set": [] }, { "ob_id": 43070, "uuid": "3d499e87d5d746c3b98dd23cfb133807", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_DMPS_LABdata", "numberOfFiles": 5, "volume": 91925, "fileFormat": "Data are BADC-CSV formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41356, "uuid": "25e39573126840d5bbb843902afc729b", "short_code": "ob", "title": "Differential Mobility Particle Sizer (DMPS) data from SASSO project - Version 0", "abstract": "This dataset contains number size distribution data from the University of Manchester Differential Mobility Particle Sizer (DMPS). The custom-built DMPS measured the evolution of size distribution for polydisperse particles formed from the ozonolysis of α-pinene (13/01/2020) or cresol (10/01/2020) in the Manchester aerosol chamber. Additionally, similar measurements were conducted under the same conditions but with black carbon (BC) as the seed (23/01/2020: BC + α-pinene; 22/01/2020: BC + cresol). These data were collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. This information can reveal the evolution of the particles inside the chamber.\r\n\r\nThis version 0 dataset contains particle size distribution. The DMPS is operated by University of Manchester scientists." }, "onlineresource_set": [] }, { "ob_id": 43072, "uuid": "8eac9b764ec94b388f4bc7243006559a", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/1km", "numberOfFiles": 6505, "volume": 324702016098, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42324, "uuid": "b963ead70580451aa7455782224479d5", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 1km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The datasets cover the UK at 1 km x 1 km resolution. These 1 km x 1 km data have been used to provide a range of other resolutions and across countries, administrative regions and river basins to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution. \r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43073, "uuid": "6a4999c527ef46dcac5c7c64c4118038", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/river", "numberOfFiles": 163, "volume": 24969594, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42326, "uuid": "b1282951f38947da93c0b0db31bb8419", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK river basins, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK river basins consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43074, "uuid": "890c1922f743463ab2d2692479f2d872", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/5km", "numberOfFiles": 6505, "volume": 14705927716, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42322, "uuid": "5ba67d62cdc249a3bc5b1c38b339beb3", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 5km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 5 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43075, "uuid": "dd0626e6218a4772b15e1ba561850b07", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/12km", "numberOfFiles": 6505, "volume": 2859928948, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42325, "uuid": "5a248096468640a6bfb0dfda8b018ac5", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 12km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 12 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation). \r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43076, "uuid": "6fd378970fa34297b768c2b67f5135c3", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/region", "numberOfFiles": 163, "volume": 19207679, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42328, "uuid": "8a51496be92b4e9488954c7c0199f3f9", "short_code": "ob", "title": "HadUK-Grid Climate Observations by Administrative Regions over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK administrative regions consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023 but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43077, "uuid": "c799faaea72e4c26bc656219b75996c9", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/25km", "numberOfFiles": 6505, "volume": 889161020, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42323, "uuid": "18ddbb686be549bfadfecbe0c673f405", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 25km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 25 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43078, "uuid": "9dc087c4ff8442b88cc9f5c44a65527d", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/60km", "numberOfFiles": 6505, "volume": 336410037, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42321, "uuid": "c22d0b462321447882d2d1367cc77d3c", "short_code": "ob", "title": "HadUK-Grid Gridded Climate Observations on a 60km grid over the UK, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. The dataset at 60 km resolution is derived from the associated 1 km x 1 km resolution to allow for comparison to data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n \r\n* Added data for calendar year 2023\r\n \r\n* Added newly digitised data for daily rainfall (62 Scottish stations for 1945-1960)\r\n \r\n* Daily rainfall data for Bolton, 1916-1919 have been corrected (previous values were corrupted and needed redigitising)\r\n \r\n* Daily rainfall data for Buxton, 1960 have been corrected (conversion from inches to mm had been applied incorrectly)\r\n \r\n* Rainfall data from EA and SEPA APIs are included for the last three months of the dataset (Oct-Dec 2023) (for all earlier months the rainfall data from partner agencies is obtained from the Met Office's MIDAS database)\r\n \r\n* The number of stations used for groundfrost, sunshine and windspeed have reduced at different points in the historical series when comparing v1.3.0.ceda to the previous version v1.2.0.ceda. These reductions in station numbers have been caused by changes made in the data processing steps upstream of the gridding process.\r\n \r\n* For groundfrost this reduction has been caused by an automated quality control process flagging the historical data which have been removed as suspect (mostly affecting data from 1961 to 1970).\r\n \r\n* For sunshine the small reduction in the 1960s has been caused by the removal of digitized monthly sunshine data through this period where we wish to reverify the data source.\r\n \r\n* For windspeed the reduction from 1969 to 2010 has been caused by changes to rules applied relating to data completeness when compiling daily mean windspeeds, which in turn have followed through to monthly statistics.\r\n \r\n* We plan to carry out a review of the data which have been excluded from this version. Some of it may be reintroduced in a future release.\r\n \r\n* Net changes to the input station data:\r\n \r\n- Total of 126970983 observations\r\n- 125384735 (98.75%) unchanged\r\n- 28487 (0.02%) modified for this version\r\n- 1557761 (1.23%) added in this version\r\n- 188522 (0.15%) deleted from this version\r\n \r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43080, "uuid": "809c118c5c384617b1fd7b212d8c09b1", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_09/ch9_ccb9_1_fig1/v20230523", "numberOfFiles": 7, "volume": 26703, "fileFormat": "CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 43086, "uuid": "94af2313699a421a8499689180812fb8", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ar6_wg1/data/ch_03/ch3_ccb1_fig1/v20220615", "numberOfFiles": 7, "volume": 153726, "fileFormat": "csv, nc", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": null, "onlineresource_set": [] }, { "ob_id": 43089, "uuid": "5cd38adfc88d451a8be58c89445c294f", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadUK-Grid/v1.3.0.ceda/country", "numberOfFiles": 163, "volume": 12786131, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42327, "uuid": "a508838f92c74005a26b9277eae59a7c", "short_code": "ob", "title": "HadUK-Grid Climate Observations by UK countries, v1.3.0.ceda (1836-2023)", "abstract": "HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations. The data have been interpolated from meteorological station data onto a uniform grid to provide complete and consistent coverage across the UK. These data at 1 km resolution have been averaged across a set of discrete geographies defining UK countries consistent with data from UKCP18 climate projections. The dataset spans the period from 1836 to 2023, but the start time is dependent on climate variable and temporal resolution.\r\n\r\nThe gridded data are produced for daily, monthly, seasonal and annual timescales, as well as long term averages for a set of climatological reference periods. Variables include air temperature (maximum, minimum and mean), precipitation, sunshine, mean sea level pressure, wind speed, relative humidity, vapour pressure, days of snow lying, and days of ground frost.\r\n\r\nThis data set supersedes the previous versions of this dataset which also superseded UKCP09 gridded observations. Subsequent versions may be released in due course and will follow the version numbering as outlined by Hollis et al. (2019, see linked documentation).\r\n\r\nThe changes for v1.3.0.ceda HadUK-Grid datasets are as follows:\r\n\r\n * Added data for calendar year 2023\r\n \r\n* Added newly digitised data for monthly sunshine 1910-1918\r\n\r\n * Added Rainfall Rescue version 2 doi:10.5281/zenodo.7554242\r\n\r\n * Updated shapefiles used for production of area average statistics https://github.com/ukcp-data/ukcp-spatial-files\r\n\r\n * Updated controlled vocabulary for metadata assignment https://github.com/ukcp-data/UKCP18_CVs\r\n\r\n * Updated assignment of timepoint for some periods so that the datetime is the middle of the period (e.g. season) rather than a fixed offset from the period start.\r\n\r\n * Updated ordering of regions within regional values files. Alphabetical ordering.\r\n\r\n * Files use netcdf level 4 compression using gzip https://www.unidata.ucar.edu/blogs/developer/entry/netcdf_compression\r\n\r\n* Net changes to the input station data used to generate this dataset:\r\n\r\n- Total of 125601744 observations\r\n\r\n- 122621050 (97.6%) unchanged\r\n\r\n- 26700 (0.02%) modified for this version\r\n\r\n- 2953994 (2.35%) added in this version\r\n\r\n- 16315 (0.01%) deleted from this version\r\n\r\n* Changes to monthly rainfall 1836-1960\r\n\r\n- Total of 4823973 observations\r\n\r\n- 3315657 (68.7%) unchanged\r\n\r\n- 21029 (0.4%) modified for this version\r\n\r\n- 1487287 (30.8%) added in this version\r\n\r\n- 11155 (0.2%) deleted from this version\r\n\r\nThe primary purpose of these data are to facilitate monitoring of UK climate and research into climate change, impacts and adaptation. The datasets have been created by the Met Office with financial support from the Department for Business, Energy and Industrial Strategy (BEIS) and Department for Environment, Food and Rural Affairs (DEFRA) in order to support the Public Weather Service Customer Group (PWSCG), the Hadley Centre Climate Programme, and the UK Climate Projections (UKCP18) project. The output from a number of data recovery activities relating to 19th and early 20th Century data have been used in the creation of this dataset, these activities were supported by: the Met Office Hadley Centre Climate Programme; the Natural Environment Research Council project \"Analysis of historic drought and water scarcity in the UK\"; the UK Research & Innovation (UKRI) Strategic Priorities Fund UK Climate Resilience programme; The UK Natural Environment Research Council (NERC) Public Engagement programme; the National Centre for Atmospheric Science; National Centre for Atmospheric Science and the NERC GloSAT project; and the contribution of many thousands of public volunteers. The dataset is provided under Open Government Licence." }, "onlineresource_set": [] }, { "ob_id": 43092, "uuid": "d751441dfece47c1bc91ada5796cb351", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/biomass/data/agb/maps/v5.01/", "numberOfFiles": 8978, "volume": 663753419014, "fileFormat": "Data are netCDF and geotiff formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43090, "uuid": "bf535053562141c6bb7ad831f5998d77", "short_code": "ob", "title": "ESA Biomass Climate Change Initiative (Biomass_cci): Global datasets of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021, v5.01", "abstract": "This dataset comprises estimates of forest above-ground biomass for the years 2010, 2015, 2016, 2017, 2018, 2019, 2020 and 2021. They are derived from a combination of Earth observation data, depending on the year, from the Copernicus Sentinel-1 mission, Envisat’s ASAR (Advanced Synthetic Aperture Radar) instrument and JAXA’s (Japan Aerospace Exploration Agency) 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\nThis release of the data is version 5. Compared to version 4, version 5 consists of an update of the three maps of AGB (aboveground biomass) for the years 2010, 2017, 2018, 2019, 2020 and new AGB maps for 2015, 2016 and 2021. New AGB change maps have been created for consecutive years (2015-2016, 2016-2017 and 2020-2021), alongside an update of change maps for years 2010-2020, 2017-2018, 2018-2019 and 2019-2020, and for a decadal interval (2020-2010). The pool of remote sensing data now includes multi-temporal observations at L-band for all biomes and for all years. The AGB maps rely on revised allometries which are now based on a longer record of spaceborne LiDAR data from the GEDI and ICESat-2 missions. Temporal information is now implemented in the retrieval algorithm to preserve biomass dynamics as expressed in the remote sensing data. Biases between 2010 and more recent years have been reduced.\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\nAdditionally provided in this version release are new aggregated data products. These aggregated products of the AGB and AGB change data layers are available at coarser resolutions (1, 10, 25 and 50km).\r\n\r\nIn addition, files describing the AGB change between two consecutive years (i.e., 2015-2016, 2016-2017, 2018-2017, 2019-2018, 2019-2020, 2020-2021) and over a decade (2020-2010) are provided (labelled as 2015_2016, 2016_2017, 2017_2018, 2018_2019, 2019_2020 and 2020_2010). Each AGB change product consists of two sets of maps: the standard deviation of the AGB change and a quality flag of the AGB change. Note that the change itself can be simply computed as the difference between two AGB maps, so is not provided directly.\r\n\r\n\r\nData are provided in both netcdf and geotiff format.\r\n\r\nThis version represents an update of v5.0 which was missing a number of tiles covering islands on the Pacific and Indian Ocean and one tile covering Scandinavia north of 70 deg latitude." }, "onlineresource_set": [] }, { "ob_id": 43094, "uuid": "ef17ccbbad1c419cb0b1e96dc7a2f7ca", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_jra/cru_jra_2.5/", "numberOfFiles": 1231, "volume": 416892512154, "fileFormat": "The data are provided as gzipped NetCDF files, with one file per variable, per year.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43093, "uuid": "43ce517d74624a5ebf6eec5330cd18d5", "short_code": "ob", "title": "CRU JRA v2.5: A forcings dataset of gridded land surface blend of Climatic Research Unit (CRU) and Japanese reanalysis (JRA) data; Jan.1901 - Dec.2023.", "abstract": "The CRU JRA V2.5 dataset is a 6-hourly, land surface, gridded time series of ten meteorological variables produced by the Climatic Research Unit (CRU) at the University of East Anglia (UEA), and is intended to be used to drive models. The variables are provided on a 0.5 degree latitude x 0.5 degree longitude grid, the grid is near global but excludes Antarctica (this is the same as the CRU TS grid, though the set of variables is different). The data are available at a 6 hourly time-step from January 1901 to December 2023.\r\n\r\nThe dataset is constructed by regridding data from the Japanese Reanalysis data (JRA) produced by the Japanese Meteorological Agency (JMA), adjusting where possible to align with the CRU TS 4.08 data (see the Process section and the ReadMe file for full details).\r\n\r\nThe CRU JRA data consists of the following ten meteorological variables: 2-metre temperature, 2-metre maximum and minimum temperature, total precipitation, specific humidity, downward solar radiation flux, downward long wave radiation flux, pressure and the zonal and meridional components of wind speed (see the ReadMe file for further details).\r\n\r\nThe CRU JRA dataset is intended to be a replacement of the CRU NCEP forcing dataset. The CRU JRA dataset follows the style of Nicolas Viovy's original CRU NCEP dataset rather than that which is available from UCAR. A link to the CRU NCEP documentation for comparison is provided in the documentation section. \r\nThis version of CRUJRA, v2.5 (1901-2023) is, where possible, adjusted to align with CRU TS monthly means or totals. A consequence of this is that, if CRU TS changes, then CRUJRA changes.\r\n\r\nFor this version, and version 4.07 of CRU TS, the CLD (cloud cover, %) variable is now actualised (converted from gridded anomalies) using the original CLD climatology and not the revised climatology introduced last year. This change/reversion is summarised here: https://crudata.uea.ac.uk/cru/data/hrg/cru_cl_1.1/Read_Me_CRU_CL_CLD_Reversion.txt\r\n\r\nSince CLD is used to align DSWRF, CRUJRA Downward Short Wave Radiation Flux (DSWRF) will now be 'closer to' version 2.2 and earlier and should be used in preference to v2.3.\r\n\r\nIf this dataset is used in addition to citing the dataset as per the data citation string users must also cite the following:\r\n\r\nHarris, I., Osborn, T.J., Jones, P. et al. Version 4 of the CRU TS\r\nmonthly high-resolution gridded multivariate climate dataset.\r\nSci Data 7, 109 (2020). https://doi.org/10.1038/s41597-020-0453-3\r\n\r\nHarris, I., Jones, P.D., Osborn, T.J. and Lister, D.H. (2014), Updated\r\nhigh-resolution grids of monthly climatic observations - the CRU TS3.10\r\nDataset. International Journal of Climatology 34, 623-642.\r\n\r\nKobayashi, S., et. al., The JRA-55 Reanalysis: General Specifications and\r\nBasic Characteristics. J. Met. Soc. Jap., 93(1), 5-48\r\nhttps://dx.doi.org/10.2151/jmsj.2015-001" }, "onlineresource_set": [] }, { "ob_id": 43098, "uuid": "ca8c0fc882144c9bb7e8214c9179e462", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2024/FAFMIP_HadCM3_HadGEM2-ES", "numberOfFiles": 324, "volume": 195312084544, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43099, "uuid": "0211e7f5f6354cfca18fd15b974b2e5f", "short_code": "ob", "title": "FAFMIP_HadCM3_HadGEM2-ES", "abstract": "Data from HadCM3 and HadGEM2 in supporting the Thresholds for the future of the Greenland ice-sheet project (grant NE/P014976/1)" }, "onlineresource_set": [] }, { "ob_id": 43101, "uuid": "ee067768f3404248be8d76b6f34c8613", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_ts/cru_ts_4.08", "numberOfFiles": 587, "volume": 20900632631, "fileFormat": "Data are provided in ASCII and NetCDF formats.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43100, "uuid": "715abce1604a42f396f81db83aeb2a4b", "short_code": "ob", "title": "CRU TS4.08: Climatic Research Unit (CRU) Time-Series (TS) version 4.08 of high-resolution gridded data of month-by-month variation in climate (Jan. 1901- Dec. 2023)", "abstract": "The gridded Climatic Research Unit (CRU) Time-series (TS) data version 4.08 data are month-by-month variations in climate over the period 1901-2023, provided on high-resolution (0.5x0.5 degree) grids, produced by CRU at the University of East Anglia and funded by the UK National Centre for Atmospheric Science (NCAS), a NERC collaborative centre.\r\n\r\nThe CRU TS4.08 variables are cloud cover, diurnal temperature range, frost day frequency, wet day frequency, potential evapotranspiration (PET), precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, and vapour pressure for the period January 1901 - December 2023.\r\n\r\nThe CRU TS4.08 data were produced using angular-distance weighting (ADW) interpolation. All versions prior to 4.00 used triangulation routines in IDL. Please see the release notes for full details of this version update. \r\n\r\nThe CRU TS4.08 data are monthly gridded fields based on monthly observational data calculated from daily or sub-daily data by National Meteorological Services and other external agents. The ASCII and NetCDF data files both contain monthly mean values for the various parameters. The NetCDF versions contain an additional integer variable, ’stn’, which provides, for each datum in the main variable, a count (between 0 and 8) of the number of stations used in that interpolation. The missing value code for 'stn' is -999.\r\n\r\nAll CRU TS output files are actual values - NOT anomalies." }, "onlineresource_set": [] }, { "ob_id": 43102, "uuid": "6374b159047c4b2b96a7980f8c9f33c2", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-radiation-obs/dataset-version-202407/", "numberOfFiles": 5768, "volume": 3264076722, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42333, "uuid": "0afba628c2f4462da68b0a81ebf1ff4c", "short_code": "ob", "title": "MIDAS Open: UK hourly solar radiation data, v202407", "abstract": "The UK hourly solar radiation data contain the amount of solar irradiance received during the hour ending at the specified time. All sites report 'global' radiation amounts. This is also known as 'total sky radiation' as it includes both direct solar irradiance and 'diffuse' irradiance as a result of light scattering. Some sites also provide separate diffuse and direct irradiation amounts, depending on the instrumentation at the site. For these the sun's path is tracked with two pyrometers - one where the path to the sun is blocked by a suitable disc to allow the scattered sunlight to be measured to give the diffuse measurement, while the other has a tube pointing at the sun to measure direct solar irradiance whilst blanking out scattered sun light. \r\n\r\nFor details about the different measurements made and the limited number of sites making them please see the MIDAS Solar Irradiance table linked to in the online resources section of this record.\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. These include the addition of data for calendar year 2023.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, MODLERAD, ESAWRADT and DRADR35 messages. The data spans from 1947 to 2023.\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 the 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." }, "onlineresource_set": [] }, { "ob_id": 43103, "uuid": "aee5bb50209e45cca1eb8d01e073509b", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-soil-temperature-obs/dataset-version-202407/", "numberOfFiles": 23948, "volume": 4264701373, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42330, "uuid": "a6bb3e8def544b5790d4b05a6f37f901", "short_code": "ob", "title": "MIDAS Open: UK soil temperature data, v202407", "abstract": "The UK soil temperature data contain daily and hourly values of soil temperatures at depths of 5, 10, 20, 30, 50, and 100 centimetres. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM or DLY3208 messages. The data spans from 1900 to 2023.\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. These include the addition of data for calendar year 2023.\r\n\r\nAt many stations temperatures below the surface are measured at various depths. The depths used today are 5, 10, 20, 30 and 100cm, although measurements are not necessarily made at all these depths at a station and exceptionally measurements may be made at other depths. When imperial units were in general use, typically before 1961, the normal depths of measurement were 4, 8, 12, 24 and 48 inches.\r\n\r\nLiquid-in-glass soil thermometers at a depth of 20 cm or less are unsheathed and have a bend in the stem between the bulb and the lowest graduation. At greater depths the thermometer is suspended in a steel tube and has its bulb encased in wax.\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 the 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." }, "onlineresource_set": [] }, { "ob_id": 43104, "uuid": "e701e8bd2c4946648016699b2a619c22", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-mean-wind-obs/dataset-version-202407/", "numberOfFiles": 15324, "volume": 8861087248, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42331, "uuid": "91cb9985a6c2453d99084bde4ff5f314", "short_code": "ob", "title": "MIDAS Open: UK mean wind data, v202407", "abstract": "The UK mean wind data contain the mean wind speed and direction, and the direction, speed and time of the maximum gust, all during 1 or more hours, ending at the stated time and date. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: SYNOP, HCM, AWSHRLY, DLY3208, HWNDAUTO and HWND6910. The data spans from 1949 to 2023.\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. These include the addition of data for calendar year 2023.\r\n\r\nFor further details on observing practice, including measurement accuracies for the message types, see relevant sections of the MIDAS User Guide linked from this record (e.g. section 3.3 details the wind network in the UK, section 5.5 covers wind measurements in general and section 4 details message type information).\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 the 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." }, "onlineresource_set": [] }, { "ob_id": 43105, "uuid": "4128e6dbbc87403793b2d4cf8ccad936", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-weather-obs/dataset-version-202407/", "numberOfFiles": 53148, "volume": 34099134895, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42332, "uuid": "c50776e4903942cdb329589da70b83fe", "short_code": "ob", "title": "MIDAS Open: UK hourly weather observation data, v202407", "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 2023.\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. These include the addition of data for calendar year 2023.\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." }, "onlineresource_set": [] }, { "ob_id": 43106, "uuid": "36f6817d68d84c4dabfed84bc8acaafa", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-temperature-obs/dataset-version-202407/", "numberOfFiles": 64760, "volume": 2223983181, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42336, "uuid": "b7c6295b72c54fa9bcd8308fea2727e7", "short_code": "ob", "title": "MIDAS Open: UK daily temperature data, v202407", "abstract": "The UK daily temperature data contain maximum and minimum temperatures (air, grass and concrete slab) measured over a period of up to 24 hours. The measurements were recorded by observation stations operated by the Met Office across the UK and transmitted within NCM, DLY3208 or AWSDLY messages. The data span from 1853 to 2023. For details on measurement techniques, including calibration information and changes in measurements, see section 5.2 of the MIDAS User Guide linked to from this record. Soil temperature data may be found in the UK soil temperature datasets linked from this record.\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. These include the addition of data for calendar year 2023.\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 the 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. Currently this represents approximately 95% of available daily temperature observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 43107, "uuid": "19524ed9b7db49e89e7d4895b791edb5", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-hourly-rain-obs/dataset-version-202407/", "numberOfFiles": 17969, "volume": 7276133977, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42334, "uuid": "6c619c67138843b8839a5788ac749e12", "short_code": "ob", "title": "MIDAS Open: UK hourly rainfall data, v202407", "abstract": "The UK hourly rainfall data contain the rainfall amount (and duration from tilting syphon gauges) during the hour (or hours) ending at the specified time. The data also contains precipitation amounts, however precipitation measured over 24 hours are not stored. Over time a range of rain gauges have been used - see the linked MIDAS User Guide for further details.\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.\r\n\r\nThe data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSHRLY, DLY3208, SREW and SSER. The data spans from 1915 to 2023.\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 the 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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset." }, "onlineresource_set": [] }, { "ob_id": 43108, "uuid": "80e1e1e4377a4d58995ec3e563aec2ad", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-weather-obs/dataset-version-202407/", "numberOfFiles": 50177, "volume": 3145644557, "fileFormat": "Data are BADC-CSV formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42335, "uuid": "8070d47e1b7340468fa7cf654dee938b", "short_code": "ob", "title": "MIDAS Open: UK daily weather observation data, v202407", "abstract": "The UK daily weather observation data contain meteorological values measured on a 24 hour time scale. The measurements of sunshine duration, concrete state, snow depth, fresh snow depth, and days of snow, hail, thunder and gail were attained by observation stations operated by the Met Office across the UK operated and transmitted within DLY3208, NCM, AWSDLY and SYNOP messages. The data span from 1887 to 2023. For details of observations see the relevant sections of the MIDAS User Guide linked from this record for the various message types.\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. These include the addition of data for calendar year 2023.\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 the 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. Currently this represents approximately 95% of available daily weather observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 43109, "uuid": "eb0446136a6144148fbbd697f0fa7987", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/ukmo-midas-open/data/uk-daily-rain-obs/dataset-version-202407/", "numberOfFiles": 273626, "volume": 5638778351, "fileFormat": "Data are BADC-CSV formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 42337, "uuid": "8606115371e44b079e25d479cfec465c", "short_code": "ob", "title": "MIDAS Open: UK daily rainfall data, v202407", "abstract": "The UK daily rainfall data contain rainfall accumulation and precipitation amounts over a 24 hour period. The data were collected by observation stations operated by the Met Office across the UK and transmitted within the following message types: NCM, AWSDLY, DLY3208 and SSER. The data spans from 1853 to 2023. Over time a range of rain gauges have been used - see section 5.6 and the relevant message type information in the linked MIDAS User Guide for further details.\r\n\r\nThis version supersedes the previous version (202308) 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. These include the addition of data for calendar year 2023.\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. A large proportion of the UK raingauge observing network (associated with WAHRAIN, WADRAIN and WAMRAIN for hourly, daily and monthly rainfall measurements respectively) is operated by other agencies beyond the Met Office, and are consequently currently excluded from the Midas-open dataset. Currently this represents approximately 13% of available daily rainfall observations within the full MIDAS collection." }, "onlineresource_set": [] }, { "ob_id": 43115, "uuid": "62ae01e501154d4dba21fb7ac0631f14", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/moya/data/stations/uganda-jinja/MOYA_EM27SUN_Jinja", "numberOfFiles": 2, "volume": 2300519, "fileFormat": "netCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43114, "uuid": "7a8d0936ba1e4e1a8689c9e9010b43b2", "short_code": "ob", "title": "Ground-based greenhouse gas column concentrations from Jinja, Uganda, January to April 2020", "abstract": "These data comprise remotely sensed column concentrations of greenhouse gases over Jinja, Uganda, covering the period spanning January to April 2020. A Bruker EM27/SUN spectrometer and solar tracker were used to make the measurements, which were then processed into column concentrations of carbon dioxide, methane, and carbon monoxide using the PROFFAST retrieval code developed through the COllaborative Carbon Column Observing Network (COCCON) programme at the Karlsruhe Institute of Technology. The data were collected to provide a source of validation for satellite data products, and for outputs from atmospheric chemistry and transport models. The data were collected and processed by Neil Humpage at the University of Leicester, in collaboration with William Okello at the National Fisheries Resources Research Institute who provided the measurement site." }, "onlineresource_set": [] }, { "ob_id": 43121, "uuid": "b3677fc334a04107b6273eec2ba3d516", "short_code": "result", "curationCategory": "", "dataPath": "/badc/cru/data/cru_cy/cru_cy_4.08/", "numberOfFiles": 2924, "volume": 52236305, "fileFormat": "The CRU CY data are provided as text files with the extension \".per\", most text editors will open these files. See the linked file formats guide for more information.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43120, "uuid": "3b7f475a30a642e9af5323cef748bb00", "short_code": "ob", "title": "CRU CY4.08: Climatic Research Unit year-by-year variation of selected climate variables by country version 4.08 (Jan. 1901 - Dec. 2023)", "abstract": "The Climatic Research Unit (CRU) Country (CY) data version 4.08 dataset consists of ten climate variables for country averages at a monthly, seasonal and annual frequency: including cloud cover, diurnal temperature range, frost day frequency, precipitation, daily mean temperature, monthly average daily maximum and minimum temperature, vapour pressure, potential evapotranspiration and wet day frequency. This version uses the updated set of country definitions, please see the appropriate Release Notes.\r\n\r\nThis dataset was produced in 2024 by CRU at the University of East Anglia and extends the CRU CY4.07 data to include 2023. The data are available as text files with the extension '.per' and can be opened by most text editors.\r\n\r\nSpatial averages are calculated using area-weighted means. CRU CY4.08 is derived directly from the CRU time series (TS) 4.07 dataset. CRU CY version 4.08 spans the period 1901-2023 for 292 countries.\r\n\r\nTo understand the CRU CY4.08 dataset, it is important to understand the construction and limitations of the underlying dataset, CRU TS4.07. It is therefore recommended that all users read the Harris et al, 2020 paper and the CRU TS4.08 release notes listed in the online documentation on this record.\r\n\r\nCRU CY data are available for download to all CEDA users." }, "onlineresource_set": [] }, { "ob_id": 43123, "uuid": "f791d5a3b6e54806937bf0ae8ec15778", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/tccon/tccon_mirror/data/bialystok01", "numberOfFiles": 2, "volume": 97847900, "fileFormat": "netcdf", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43122, "uuid": "494d0131d23f4ca88ef3494582362015", "short_code": "ob", "title": "Total Carbon Column Observing Network (TCCON):TCCON data from Bialystok (PL), Release GGG2020.R0", "abstract": "The Total Carbon Column Observing Network (TCCON) is a network of ground-based Fourier Transform Spectrometers that record direct solar absorption spectra of the atmosphere in the near-infrared. From these spectra, accurate and precise column-averaged abundances of atmospheric constituents including carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), hydrogen fluoride (HF), carbon monoxide (CO), water vapour (H2O), and deuterated water vapour (HDO), are retrieved. This is the GGG2020 data release of observations from the TCCON station at Bialystok, Poland." }, "onlineresource_set": [] }, { "ob_id": 43126, "uuid": "48ac3297cab14cd485bb73e085827b99", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/OpenCLIM_UrbanFlood_Model/", "numberOfFiles": 894, "volume": 21107940992, "fileFormat": "Files are BADC-CSV and TIFF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43054, "uuid": "a798f69cb13e4c219e735f96a7c33faa", "short_code": "ob", "title": "OpenCLIM - Urban Flood Impacts : 1km gridded outputs for five GB cities", "abstract": "This dataset contains output datasets from the OpenCLIM_UrbanFlood Workflow. These runs were part of the NERC funded OpenCLIM (Open CLimate IMpacts modelling framework) project. This data can be used for the continued analysis of climate impacts and for comparison with future studies.\r\n\r\nThe data has been generated using the OpenCLIM_UrbanFlood workflow available on DAFNI (https://www.dafni.ac.uk/) and focuses on the cities of Newcastle, Norwich, Bath, Inverness and Swansea. The workflow was run for five cities across GB to analyse changes in flood risk for a large range of urban and climate futures. The workflow uses output datasets from the Urban Development Model (described below) to generate future urban landscapes. \r\n\r\nA second model transformed the population density data into new urban form maps including building footprints. Passed through the 2-D hydrodynamic model CityCAT, a range of future storm events with varying intensities were modelled to capture changes in flow and water depths across the domain. Damages incurred as a result of flood waters were calculated and aggregated to the 1km grid level along with the number of commercial and residential buildings affected. The code for each model in the workflow is available within the OpenCLIM GitHub repository (linked in Related Documents).\r\n\r\nThe Urban Development Model (Newcastle University) presents plausible realisations of future urban change. These are initiated from the 2017 Ordnance Survey urban-rural land use ('UDMbaseline') and projected into the future using attractors and constraints based on the UK's Shared Socio-ecconomic Pathways (SSPs)." }, "onlineresource_set": [] }, { "ob_id": 43127, "uuid": "69cdfaf066364374bb33f3b989244e14", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/icecaps-ace/data/leeds-pops-0307", "numberOfFiles": 10, "volume": 25468140, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43128, "uuid": "6b68b5e1ffd2467886386eaf0dfafd24", "short_code": "ob", "title": "ICECAPS-ACE: Vertical aerosol particle size distributions from the University of Leeds POPS 0307 instrument collected via Helikite balloon above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains vertically resolved aerosol particle size distribution measurements collected using a tethered balloon platform at Summit Station, Greenland, in July and August 2023.\r\n\r\nAerosol particle size distributions were measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0307). The POPS was placed in a lightweight insulating foam box, and a coarse mesh filter was placed over the inlet to prevent the growth of rime ice. The POPS was secured to the kite wing on the tethered balloon such that the inlet was always oriented into the wind.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "onlineresource_set": [] }, { "ob_id": 43131, "uuid": "38e154761b58422582a27c40ca1ced08", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/icecaps-ace/data/leeds-windsond-5094", "numberOfFiles": 7, "volume": 600667, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43130, "uuid": "0c18a36ee02a4598963c1f7f97acd201", "short_code": "ob", "title": "ICECAPS-ACE: radiosonde measurements from the University of Leeds Windsond unit 5094 deployed by helikite above Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains meteorology measurements (air pressure, temperature, and relative humidity) from the University of Leeds windsond unit 5094 deployed by tethered balloon above the Summit Station field site, Greenland.\r\n\r\nPost-processing of the radiosonde data revealed unrealistic temperature increases when the measurement platform was stationary, these are indicated by a quality control flag. \r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "onlineresource_set": [] }, { "ob_id": 43134, "uuid": "b5403bfd5ca343f1b34443661b02c454", "short_code": "result", "curationCategory": "", "dataPath": "/badc/icecaps-ace/data/leeds-pops-0288", "numberOfFiles": 7, "volume": 293780963, "fileFormat": "Data are NetCDF formattede", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43129, "uuid": "ceaded7386ab4fb781e5344cb94db57d", "short_code": "ob", "title": "ICECAPS-ACE: surface aerosol particle size distributions from the University of Leeds POPS 0288 instrument at Summit Station, Greenland, July-August 2023", "abstract": "This dataset contains surface aerosol particle size distribution measurements from Summit Station Greenland measured by a Handix Portable Optical Particle Spectrometer (POPS 1120, S/N: 0288). The POPS was connected to an omnidirectional total air inlet and installed on the roof of the Atmospheric Watch Observatory building at Summit Station.\r\n\r\nThese data were collected as part of the joint Natural Environmental Research Council (NERC) and US National Science Foundation (NSF) -funded Integrated Characterisation of Energy, Clouds, Atmospheric state, and Precipitation at Summit - Aerosol Cloud Experiment (ICECAPS-ACE) project." }, "onlineresource_set": [] }, { "ob_id": 43145, "uuid": "d7effea6c28d4154a8bed1b3a19be9a0", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_mass_flow_rate_ice_discharge/v2.0", "numberOfFiles": 8, "volume": 384289, "fileFormat": "csv, png, txt", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41959, "uuid": "89c654c2e4a74ce5a494b69753d8291e", "short_code": "ob", "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Mass flow rate ice discharge (MFID) for Greenland from CCI IV, CCI SEC, and BedMachine v2.0", "abstract": "Mass flow rate ice discharge (MFID) for Greenland ice sheet sectors. This data set is part of the ESA Greenland Ice sheet CCI project. \r\n\r\nIt provides the following CSV files: \r\n- Mass flow rate ice discharge. Units are Gt yr^{-1}.\r\n- Mass flow rate ice discharge uncertainty. Units are Gt yr^{-1}.\r\n- Coverage for each sector at each timestamp. Unitless [0 to 1].\r\n\r\nIce discharge is calculated from the CCI Ice Velocity (IV) product, the CCI Surface Elevation Change (SEC) product (where it overlaps with the ice discharge gates), and ice thickness from BedMachine. Ice discharge gates are placed 10 km upstream from all marine terminating glacier termini that have baseline velocities of more than 150 m/yr. Results are summed by Zwally et al. (2012) sectors.\r\n\r\nThe methods, including description of \"coverage\", are described in Mankoff et al. 2020." }, "onlineresource_set": [] }, { "ob_id": 43151, "uuid": "e36a52671b934cd3922e2d4d182f65b3", "short_code": "result", "curationCategory": "", "dataPath": "/badc/uk-decc-network/data/v24.01", "numberOfFiles": 132, "volume": 749529109, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43147, "uuid": "11163154cef4496988d45658c9cfbabf", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 24.01", "abstract": "This version 24.01 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 5488/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "onlineresource_set": [] }, { "ob_id": 43153, "uuid": "804b1c4723954fcc86f2b63044d404b9", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukcp18/data/land-eurocordex/uk-v20240104/country", "numberOfFiles": 14961, "volume": 5828760692, "fileFormat": "The data are in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43152, "uuid": "4b9a4bb5d29e4380b525e5579a277d45", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Countries for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for the countries of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "onlineresource_set": [] }, { "ob_id": 43155, "uuid": "ef2586ffd7004e85a1861f7cfb3d9996", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukcp18/data/land-eurocordex/uk-v20240104/12km", "numberOfFiles": 23376, "volume": 1326315973320, "fileFormat": "The data are in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43154, "uuid": "9e7ff32e78194ded8ec5df683e30303a", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK domain at 12 km Resolution for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains 12 km data for the UK on the OSGB (WGS84) grid.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "onlineresource_set": [] }, { "ob_id": 43157, "uuid": "4ad8059e8ec24bcfb129ff563decad6e", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukcp18/data/land-eurocordex/uk-v20240104/region", "numberOfFiles": 14961, "volume": 6979291393, "fileFormat": "The data are in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43156, "uuid": "64c373057a5f4cb7afc24a579a1e55d9", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by Administrative Regions for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for administrative regions of the UK.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "onlineresource_set": [] }, { "ob_id": 43159, "uuid": "58a36d9cb1044c7083230ed76784c2b9", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ukcp18/data/land-eurocordex/uk-v20240104/river", "numberOfFiles": 14961, "volume": 7984674125, "fileFormat": "The data are in NetCDF format.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43158, "uuid": "28f7e3c0f738453c9b945ef1b1bd3262", "short_code": "ob", "title": "EuroCORDEX-UK: Regional climate projections for the UK by River Basins for 1980-2080 (v20240104)", "abstract": "Regional climate model projections produced by the CoOrdinated Regional Downscaling EXperiment (CORDEX) and complementary to that produced by the UK Climate Projection 2018 (UKCP18) project. The data provides information on changes in climate for the UK until 2080, downscaled to a high resolution (12 km), helping to inform adaptation to a changing climate. \r\n\r\nThe projections cover the UK for a 100 year period, 1981-2080, for a high emissions scenario, RCP8.5. Each projection provides an example of climate variability in a changing climate, which is consistent across climate variables at different times and spatial locations.\r\n\r\nThis dataset contains average values of indices for major UK river basins.\r\n\r\nDataset development was funded under the UK Climate Resilience programme, which is supported by the UKRI Strategic Priorities Fund. The programme is co-delivered by the Met Office and NERC on behalf of UKRI partners AHRC, EPSRC, and ESRC." }, "onlineresource_set": [] }, { "ob_id": 43164, "uuid": "5d905b97ceda47689ab95d7bcbea617e", "short_code": "result", "curationCategory": "", "dataPath": "/badc/moya/data/stations/uganda-jinja/MOYA_GEOSChem_CH4_Jinja", "numberOfFiles": 3, "volume": 196713662, "fileFormat": "Net-CDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43139, "uuid": "7ecc607cb09747a59da6f46a0635f469", "short_code": "ob", "title": "CH4 column concentrations calculated from a high-res GEOS-Chem model run for Uganda, January to April 2020", "abstract": "These data comprise methane (CH4) column concentrations calculated from a GEOS-Chem model run, performed in a nested configuration at high spatial resolution (0.25deg x 0.3125deg latitude-longitude) centred on Uganda. The data included in the netCDF4 files cover a 6.0deg x 8.0deg box centred approximately on the Methane Observations and Yearly Assessments (MOYA) project EM27/SUN measurement site in Jinja. \r\n\r\nFor the a priori methane emissions inside the nested domain the EDGAR v4.3.2 database is used for anthropogenic emissions, the WetCHARTS dataset for emissions from wetlands, and the GFAS database for daily biomass burning emissions. The boundary conditions for the nested domain come from a global GEOS-Chem model run at lower spatial resolution (2.0deg x 2.5deg latitude-longitude). An ensemble Kalman Filter system is used to perform the inversion. Two netCDF4 files are included: one where we just use the a priori emissions to determine the CH4 fluxes in the model domain, and one where TROPOMI CH4 (satellite observation) data is used to constrain the emissions.\r\n\r\nEDGAR - Emissions Database for Global Atmospheric Research (linked in the Details/Docs section)\r\nWetCHARTs - Wetland Methane Emissions and Uncertainty (linked in the Details/Docs section)\r\nGFAS - Global Fire Assimilation System (linked in the Details/Docs section)\r\nTROPOMI - TROPOspheric Monitoring Instrument" }, "onlineresource_set": [] }, { "ob_id": 43165, "uuid": "a76538a96d834788ab7ea06bbb026066", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/moya/data/stations/uganda-jinja/MOYA_GEOSChem_CO2_Jinja", "numberOfFiles": 3, "volume": 196713645, "fileFormat": "Net-CDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43136, "uuid": "925816bd869644ad9fe9b877d8f42d30", "short_code": "ob", "title": "CO2 column concentrations from GEOS-Chem, covering East Africa from January to April 2020", "abstract": "These model data include column concentrations of carbon dioxide (CO2) calculated using the GEOS-Chem atmospheric chemistry and transport model, covering a region over East Africa centred on the Methane Observations and Yearly Assessments (MOYA) project EM27/SUN measurement site in Jinja, Uganda. The period modelled coincides with the EM27/SUN measurement dates, covering all days from 23rd January to 19th April 2020. \r\n\r\nA global GEOS-Chem model run was used on a 2.0deg x 2.5deg latitude-longitude grid with 47 vertical levels. Emissions inventories are used for the a priori flux estimates, taking into account CO2 emissions from biomass burning, fossil fuels, ocean fluxes, and biosphere fluxes. An ensemble Kalman Filter approach is used to estimate the CO2 fluxes, with either in-situ or satellite measurements of atmospheric CO2 (one .nc file for each) used as prior information on concentration. These calculations were performed by Liang Feng of the National Centre for Earth Observation, University of Edinburgh." }, "onlineresource_set": [] }, { "ob_id": 43170, "uuid": "79809028f9274d2ead5628c0a95f6a76", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/BLEACH/v20240830/", "numberOfFiles": 1490, "volume": 93502422864, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43167, "uuid": "3889a9e536c04057a98a47db21602b62", "short_code": "ob", "title": "GCHP global modelling output dataset for OH + NO2 (2021-2022)", "abstract": "These files contain model output for “Water dependence of OH+NO2 reaction reduces atmospheric oxidation” Winiberg et al., 2024. It was funded by the NERC BLEACH project (NE/W00724X/1).\r\n\r\nThe datasets are GEOS-Chem High Performance (GCHP) global model simulations of monthly mean or hourly mean of volume mixing ratios of atmospheric chemical species (e.g. Ozone (O3), PM2.5, hydroxide (OH), nitrogen dioxide (NO2) etc.) and of reaction rates of chemical reaction OH + NO2. For example, simulated O3 mixing ratio is represented by SpeciesConcVV_O3 in the netCDF files. \r\n\r\nFiles are COARDS compliant NetCDF generated by GEOS-Chem v14.2.2 to explore the global and regional impacts of changes to the OH+NO2 rate. There are 3 sets of data which each contain model output driven by two chemical mechanisms: standard mechanism in GCHP14.2.2 (directories starting 'STD_') and new mechanism with an updated rate constant (directories starting 'New_'). The calculation of reaction rate of OH+NO2 in new mechanism is updated based on the most recent measurements from JPL (NASA Jet Propulsion Laboratory). More details can be found in Winiberg et al., 2024.\r\n\r\nThe 3 sets of data included:\r\n1) Global monthly means from July 2021 to June 2022 at C48 resolution (~200km grid resolution) on 72 vertical levels (directories STD_Global/, New_Global/)\r\n\r\n2) The same but with anthropogenic emissions switched off to represent the preindustrial (directories STD_Global_anthOFF/, New_Global_anthOFF/)\r\n\r\n3) Global hourly values at the surface for July 2022 at C200 with a Stretch Factor of 2 centred on Asia (20°N 98°E) (~25km grid resolution) (directories STD_nested_surface_level/, New_nested_surface_level/)\r\n\r\nThe file names generally follows GEOS-Chem naming convention GEOSChem.DefaultCollection.YYYYMMDD_HHMMz.nc4. YYYY, MM, DD, HH, MM represent year, month, day, hour and minute. Z means UTC here. Surface data file has an extension (Surface_level_) at the beginning of the filename, which is manually added by Hansen Cao et al. to differentiate from the files for all vertical layers.\r\n\r\nThe modelling was done by Hansen Cao, Killian Murphy and Mat Evans at the University of York.\r\n\r\nCOARDS stands for the Cooperative Ocean/Atmosphere Research Data Service and are a set of conventions for the standardization of NetCDF files." }, "onlineresource_set": [] }, { "ob_id": 43178, "uuid": "3932e8b309e14a65b695786c67e21a34", "short_code": "result", "curationCategory": "", "dataPath": "/badc/gpm/data/GPM-IMERG-v7/", "numberOfFiles": 879438, "volume": 3431042407063, "fileFormat": "Data are HDF5 formatted.", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43176, "uuid": "6ae3dc8d92444b2bb954173fe98559b6", "short_code": "ob", "title": "Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) L3 half-hourly 0.1 degree x 0.1 degree v7", "abstract": "This dataset contains Global Precipitation Measurements (GPM) Integrated Multi-satellitE Retrievals (IMERG) v7. NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation over most of the Earth's surface. IMERG is particularly valuable over areas of Earth's surface that lack ground-based precipitation-measuring instruments, including oceans and remote areas. \r\n\r\nIMERG fuses precipitation estimates collected during the TRMM satellite’s operation (2000 - 2015) with recent precipitation estimates collected by the GPM mission (2014 - present) creating a continuous precipitation dataset spanning over two decades. This extended record allows scientists to compare past and present precipitation trends, enabling more accurate climate and weather models and a better understanding of Earth’s water cycle and extreme precipitation events. IMERG is available in near real-time with estimates of Earth’s precipitation updated every half-hour, enabling a wide range of applications to help communities around the world make informed decisions for disasters, disease, resource management, energy production, food security, and more.\r\n\r\nThe 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." }, "onlineresource_set": [] }, { "ob_id": 43184, "uuid": "db2448bd9fc6472f93096d0b2f74f495", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/aatsr_multimission/aatsr-v4/data/AT_1_RBT", "numberOfFiles": 101413, "volume": 53685033144689, "fileFormat": "NetCdf", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43183, "uuid": "6f5eb4ba74fa41bfb421d828d9a3f941", "short_code": "ob", "title": "AATSR: Multimission land and sea surface temperature data, 4th Reprocessing (v4) AT_1_RBT", "abstract": "Advanced Along-Track Scanning Radiometer (AATSR) mission was funded jointly by the UK Department of Energy and Climate Change External Link (DECC) and the Australian Department of Innovation, Industry, Science and Research External Link (DIISR).\r\n\r\nThe Envisat AATSR Level 1B Brightness Temperature/Radiance product (RBT) contains top of atmosphere (TOA) brightness temperature (BT) values for the infra-red channels and radiance values for the visible channels, on a 1-km pixel grid. Values for each channel and for the nadir and oblique views occupy separate NetCDF files within the Sentinel-SAFE format, along with associated uncertainty estimates. Additional files contain cloud flags, land and water masks, and confidence flags for each image pixel, as well as instrument and ancillary meteorological information.\r\n\r\nThis AATSR product (AT_1_RBT) in NetCDF format stemming from the 4th AATSR reprocessing, is a continuation of ERS ATSR data and a precursor of Sentinel-3 SLSTR data. It has replaced the former L1B product [ATS_TOA_1P] in Envisat format from the 3rd reprocessing. Users with Envisat-format products are recommended to move to the new Sentinel-SAFE like/NetCDF format products. \r\n\r\nThe 4th reprocessing of Envisat AATSR data was completed in 2022." }, "onlineresource_set": [] }, { "ob_id": 43185, "uuid": "270715d9e0d14a8ebb897ba8288665b1", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/deposited2024/TOMCAT_Nord_Stream_Methane/", "numberOfFiles": 2, "volume": 85528111, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43124, "uuid": "e41965a32923498396fd8a8446f066f1", "short_code": "ob", "title": "TOMCAT simulated Nord Stream methane plume, September 2022", "abstract": "This file contains the simulated atmospheric methane (CH4) mixing ratios over the North Sea and Northern Europe during the Nord Stream gas leak event during September 2022. Mixing ratios are provide on the TOMCAT T106 model grid, with a horizontal resolution of approximately 1.125 x 1.125 degrees. There are 60 vertical levels from the surface up to 0.1 hPa. The data covers the period from 00:00 UTC 26/09/2022 - 00:00 UTC 30/09/2022. There are two methane tracers, one containing background methane and methane from non-Nord Stream related sources, and a separate model tracer simulating CH4 from the Nord Stream leaks. For this simulation, Nord Stream was assumed to emit methane at a constant rate of 4.17 Gg hr^(-1). These simulations are discussed in Wilson et al., (2024) - 'Quantifying large methane emissions from the Nord Stream pipeline gas leak of September 2022 using IASI satellite observations and inverse modelling'." }, "onlineresource_set": [] }, { "ob_id": 43189, "uuid": "77fe242e96e14c4eafe6630ad41bd091", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/birmingham_urban_observatory/", "numberOfFiles": 132, "volume": 779971542, "fileFormat": "BADC CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43190, "uuid": "66b2138784de4514a57c3f8da8fe14cc", "short_code": "ob", "title": "Birmingham Urban Observatory & West Midlands-Air Air Quality Data", "abstract": "This dataset contains air quality data (PM2.5 concentrations) from a series of low-cost sensors deployed by Birmingham Urban Observatory & West Midlands Air.\r\n\r\nThese sensors record PM2.5 in ugm-3. \r\n\r\nformat: Data are CSV formatted" }, "onlineresource_set": [] }, { "ob_id": 43197, "uuid": "f7802eb341234d8597904b20991ea4cf", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/uk-decc-network/data/v24.09", "numberOfFiles": 68, "volume": 1557515026, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43187, "uuid": "bd7164851bcc491b912f9d650fcf7981", "short_code": "ob", "title": "Atmospheric trace gas observations from the UK Deriving Emissions linked to Climate Change (DECC) Network and associated data - Version 24.09", "abstract": "This version 24.09 dataset consists of atmospheric trace gas observations made as part of the UK Deriving Emissions linked to Climate Change (DECC) Network. It includes core DECC Network measurements, funded by the UK Government Department for Energy Security and Net Zero (TRN: 5488/11/2021) and through the National Measurement System at the National Physical Laboratory, supplemented by observations funded through other associated projects. \r\nThe core DECC network consists of five sites in the UK and Ireland measuring greenhouse and ozone-depleting gases. The four UK-based sites (Ridge Hill, Herefordshire; Tacolneston, Norfolk; Bilsdale, North Yorkshire; and Heathfield, East Sussex) sample air from elevated inlets on tall telecommunications towers. Mace Head, situated on the west coast of Ireland, samples from an inlet 10 metres above ground level and is ideally situated to intercept baseline air from the North Atlantic Ocean. The measurement site at Weybourne, Norfolk, funded by the National Centre for Atmospheric Science (NCAS) and operated by the University of East Anglia, is also affiliated with the network. Mace Head and Weybourne data are archived separately - see links in documentation. Data from the UK DECC network are used to assess atmospheric trends and quantify UK emissions, and feed into other international research programs, including the Integrated Carbon Observation System (ICOS) and Advanced Global Atmospheric Gases Experiment (AGAGE) networks." }, "onlineresource_set": [] }, { "ob_id": 43200, "uuid": "c2ec883decd34b29b59c3165c72e97df", "short_code": "result", "curationCategory": "", "dataPath": "/badc/name_nwp/data/uk/UM1p5km_Mk3/", "numberOfFiles": 684687, "volume": 5312691942982, "fileFormat": "Data are in packed PP format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43199, "uuid": "6d490accd64a4290b9413d5ec94200f9", "short_code": "ob", "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk3: Feb 2015 - Jul 2017)", "abstract": "This dataset contains Numerical Weather Prediction (NWP) meteorological data produced by the operational UKV (United Kingdom Variable-resolution) configuration of the Met Office Unified Model. The files in this dataset have been processed into a form suitable for use in the Met Office NAME (Numerical Atmospheric-dispersion Modelling Environment) dispersion model. NAME uses the Met Office Numerical Weather Prediction model outputs as its source for weather data to be able to predict movement of atmospheric parcels forwards and backwards in time.\r\n\r\nThe files contain a basic collection of model-level fields (3-d winds, temperature, etc.) and a selection of single-level fields including mean sea level pressure, cloud and precipitation from the inner, fixed-resolution domain of the UKV model (this covers the UK area at a spatial resolution of 1.5 km). The UKV model uses a rotated-pole coordinate system. Fields are split into various geographical regions (referred to as \"parts\" or \"PTs\" in NAME) with separate files for each \"part\". Data are provided at hourly resolution for the period Feb 2015 - Jul 2017. All files are in packed PP format.\r\n\r\nThe NWP data used by NAME is different from other forms of Met Office NWP as follows:\r\n- It has been split into spatial partitions (i.e. different parts of the world/domain are in different files)\r\n- It has been reformatted into PP format\r\n\r\nHowever, from the perspective of the raw data, this dataset of UK gridded NWP meteorological data is generically useful for a whole range of scientific research and applications." }, "onlineresource_set": [] }, { "ob_id": 43201, "uuid": "6513b8e71ab546c79ef3c844ebb34d73", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2024/sjclim/", "numberOfFiles": 11814, "volume": 660617190430, "fileFormat": "Data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41617, "uuid": "4aac4f8ba15f43e59eb81756b464c9fb", "short_code": "ob", "title": "A global climatology of sting-jet cyclones: TRACK files and Sting-Jet Precursor Cut-Outs", "abstract": "This dataset contains:\r\n(1) extra-tropical cyclone tracks generated by the \"TRACK\" storm tracking algorithm (Hodges 1994, 1995, 1999) applied the European Centre for Medium-Range Weather Forecasts ERA5 reanalysis product,\r\n(2) cyclone 'cutouts' holding storm-centred gridded data used by the sting-jet precursor (SJP) algorithm (Martinez-Alvarado et al., 2013), and\r\n(3) output diagnostics from the SJP algorithm.\r\n\r\nThe directories contain data for seasons October-March (Northern Hemisphere) and April-September (Southern Hemisphere) during 1979-2022. TRACK uses 850 hPa relative vorticity filtered to T42 spectral resolution to find storm candidates and only tracks lasting >=1day and tracking >=500km are considered. The 10% strongest tracks also exhibiting a warm seclusion are archived in the \"storm_data\" directory for each season, except for the single season October-March 2011/2012 for which all North Atlantic tracks are also included in \"storm_data_alltracks\" directory.\r\n\r\nFull descriptions of each data type are given in:\r\n(1) readme-tracks.txt\r\n(2) readme-cutouts.txt\r\n(3) readme-sjp-outputs.txt\r\n\r\nFor full details of the storm selection and SJP analysis, see Gray et al: A global climatology of sting-jet extratropical cyclones (doi.org/10.5194/egusphere-2024-1413).\r\n\r\nSee online resources on this record for referenced items." }, "onlineresource_set": [] }, { "ob_id": 43214, "uuid": "bf09bb382bd14effabf0a67aa0c88d30", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-combined/v1.0/", "numberOfFiles": 151, "volume": 13870715, "fileFormat": "Data are in both CSV and NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41456, "uuid": "d32244e674dd438ca4d321560daad755", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Combined river discharge product, v1.0", "abstract": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci).\r\n\r\nThese river discharge time series have been computed at different locations by the combination of data derived from satellite altimeters and multispectral sensors. Two levels of combination are implemented based on the original products: Level-2, in which the data has been derived by merging multi-mission multispectral time series (called CM) and the water level product derived by radar altimeters (called Altimetry), and Level-3, in which the river discharge products obtained from altimeters and multispectral sensors are used. The river discharges are derived following several approaches:\r\n\r\n1) L2 Merged river discharge:\r\n\r\na) COPULA Altimetry – CM: by a bivariate cumulative distribution function (CDF) which is applied between the multispectral indices and the water level from altimetry to get their joint probability distribution. \r\n\r\nb) RIDESAT Altimetry - CM: by a three-parameter non-linear relationship that merges the multispectral indices and the water level from altimetry\r\n\r\n\r\n2) L3 Merged river discharge:\r\n\r\na) Altimetry - CM cal_BestFIT: by the combination of river discharges obtained by the procedure of BestFIT applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nb) Altimetry – CM cal_Copula: by the combination of river discharges obtained by the procedure of Copula applied to the multispectral and river discharges obtained by the altimetry through a weighted approach\r\n\r\nc) Altimetry – CM uncal_CDF: by the combination of river discharges obtained by the procedure of CDF applied to the multispectral and the altimetry through a weighted approach" }, "onlineresource_set": [] }, { "ob_id": 43215, "uuid": "511d8bcf94804da393a124cc61618aaf", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/river_discharge/data/RD/RD-multi/v1.2/", "numberOfFiles": 103, "volume": 10756247, "fileFormat": "Data are in both CSV and NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41457, "uuid": "a8422dd3766c447d8b5fa80920649f31", "short_code": "ob", "title": "ESA River Discharge Climate Change Initiative (RD_cci): Multispectral indices-based River Discharge Product, v1.2", "abstract": "This dataset contains river discharge (Q) data in cubic meters per second (m3/s) from the ESA Climate Change Initiative River Discharge project (RD_cci). \r\n\r\nThese river discharge time series have been computed at different locations from several satellite multispectral missions (Landsat-5, -7, -8, -9, MODIS Aqua, MODIS Terra, Sentinel-3 A/B OLCI, Sentinel-2 MSI). At each location, time series are provided for each available single sensor and then merged in a unique time series. These multi-mission, multispectral time series are also referred to as CM. The river discharges are derived following several approaches:\r\n\r\nCalibrated CM approach - best fit regression (cal-BestFit): by non-linear regression relationship between the multi-mission time series and the ground observed river discharge;\r\n\r\nCalibrated CM approach - copula regression (cal-copula): by a bivariate cumulative distribution function which is applied between the multi-mission time series and the ground observed river discharge to get their joint probability distribution;\r\n\r\nUncalibrated CM approach – CDF (uncal_CDF): by Cumulative Distribution Function curves calculated to generate the percentiles associated to the discharges from the reflectance time series." }, "onlineresource_set": [] }, { "ob_id": 43234, "uuid": "11e89aaad16744a3a17d86ee650034b1", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/obs4MIPs/DLR-BIRA/C3S-GTO-ECV-9-0/mon/toz/gn/v20231115/", "numberOfFiles": 7, "volume": 426995487, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 41552, "uuid": "a697ee6a3b314d4b866ec73d613369bb", "short_code": "ob", "title": "C3S: Obs4MIPs format GOME-type Total Ozone Essential Climate Variable (GTO-ECV), Version 9.0", "abstract": "This dataset contains a time series of monthly averaged total ozone column data from nadir-viewing satellite sensors in Obs4MIPs format. It has been reformated from Version 9 of the GOME-type Total Ozone Essential Climate Variable (GTO-ECV) product, that has been generated as part of the European Union Copernicus Climate Change Service (C3S) ozone project. \r\n\r\nGTO-ECV is generated by combining measurements from several nadir-viewing satellite sensors (GOME/ERS-2, SCIAMACHY/Envisat, OMI/Aura, GOME-2/MetOp-A, GOME-2/MetOp-B, TROPOMI/Sentinel-5P, and GOME-2/MetOp-C) into one single cohesive record. The merging approach was developed in the framework of the European Space Agency’s Climate Change Initiative (ESA-CCI) ozone project." }, "onlineresource_set": [] }, { "ob_id": 43236, "uuid": "60ff049af66349eb9743fece27ee527d", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/snow/data/swe/MERGED/v3.1", "numberOfFiles": 8972, "volume": 13620769573, "fileFormat": "NetCDF", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43202, "uuid": "9d9bfc488ec54b1297eca2c9662f9c81", "short_code": "ob", "title": "ESA Snow Climate Change Initiative (Snow_cci): Snow Water Equivalent (SWE) level 3C daily global climate research data package (CRDP) (1979 - 2022), version 3.1", "abstract": "This dataset contains v3.1 of the Daily Snow Water Equivalent (SWE) product from the ESA Climate Change Initiative (CCI) Snow project, at 0.1 degree resolution.\r\n\r\nSnow water equivalent (SWE) is the depth of liquid water that would result if the of snow cover melted completely, which equates to the snow cover mass per unit area. The SWE product covers the Northern Hemisphere from 1979/01 to 2022/05 with complex terrain, land ice, and large lakes masked. The dataset covers the Northern Hemisphere winter season (October – May; occasional data produced during June and September) at a daily frequency starting in October 1987 and every second day from 1979 to May 1987. Retrievals are not produced for coastal regions of Greenland. \r\n\r\nThe product combines passive microwave data with ground-based snow depth measurements, via Bayesian non-linear iterative assimilation, to estimate SWE. It is based on data from the recalibrated enhanced resolution CETB ESDR dataset (MEaSUREs Calibrated Enhanced-Resolution Passive Microwave Daily EASE-Grid 2.0 Brightness Temperature (CETB) Earth System Data Record (ESDR) https://nsidc.org/pmesdr/data/) resampled to the 12.5km EASE-Grid 2.0. \r\n\r\nA background snow-depth field, derived from re-gridded snow-depth observations made at synoptic weather stations, and a passive microwave emission model are the key components of the retrieval scheme. Snow density, which varies in both time and space, is parameterized from interpolated in situ observations from snow courses and snow pillows equipped with co-located snow depth sensors.\r\nThe dataset is aimed to serve the needs of users working on climate research and monitoring activities, including the detection of variability and trends, climate modelling, and aspects of hydrology and meteorology.\r\n\r\nThe Finnish Meteorological Institute is responsible for the SWE product generation. The SWE development is carried out in collaboration by FMI and Environment and Climate Change Canada (ECCC). \r\n\r\nChanges from v2.0 and v3.0\r\nv3.1 applies spatially and temporally varying snow densities within the SWE retrieval instead of during post-processing. The dry snow detection algorithm as well as the snow masking in post-production have also been updated. The time series has been extended from snow_cci version 2 by two years from 2020 to 2022. In comparison with in situ snow courses, the correlation and RMSE of v3.1 improved by 0.014 and 0.6 mm, respectively, relative to v2.0. The timing of peak snow mass is shifted two weeks later compared to v1.0 and reduction in peak snow mass presented in v2.0 is removed in v3.1. Differences between v3.0 and v.3.1 are minor, the resampling from 12.5km EASE-Grid 2.0 to the final 0.1 resolution grid has been changed for v.3.1 resulting in improved peak snow mass estimation." }, "onlineresource_set": [] }, { "ob_id": 43264, "uuid": "38fd8b50bdf94678bc02393afa1022d7", "short_code": "result", "curationCategory": "A", "dataPath": "/badc/deposited2024/Fagradalsfjall_Lava_Aerosol/", "numberOfFiles": 17, "volume": 927171, "fileFormat": "BADC-CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43226, "uuid": "f29f666e890045c783b4eef7399e9cc5", "short_code": "ob", "title": "Lava Aerosol Gas and Trace Element data from the Fagradalsfjall 2021-2023 eruption, Iceland", "abstract": "Aerosol gas and trace element data collected above vents and lava flows during the 2021-2023 Fagradaslfjall volcanic eruptions, Iceland, as well as input parameters and results for thermochemical equilibrium modelling performed in the case study.\r\n\r\nThe gas data includes those collected by Fourier transform infrared (FTIR) spectroscopy and mutiGAS instrument sampling. The trace element data was collected by filter pack sampling mounted on an Uncrewed Aerial Vehicle (UAV) and analysed by Ion Chromatography Mass Spectrometry (IC-MS) and Inductively coupled plasma mass spectrometry (ICP-MS).\r\n\r\nThese data support the paper Wainman et al., 2024 \"Trace element emissions vary with lava flow age and thermal evolution during the Fagradalsfjall 2021-2023 eruptnkedions, Iceland\" which also contains a full description of sampling method (document listed below)." }, "onlineresource_set": [] }, { "ob_id": 43266, "uuid": "7b6789cd84d84c25ae351b7ef1fda762", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/ECCC/CMAM/senD2-fix", "numberOfFiles": 96, "volume": 22166099643, "fileFormat": "DAta are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43265, "uuid": "775ba04c5819456fab59cc33f80760ca", "short_code": "ob", "title": "CCMI-2022: senD2-fix data produced by the CMAM model at CCCma", "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-fix produced by the Canadian Middle Atmosphere Model (CMAM) run by the modelling team at CCCma (Canadian Centre for Climate Modelling and Analysis) in the Climate Research Division of Environment and Climate Change Canada for the Chemistry Climate Modelling Initiative (CCMI).\r\n\r\nIt used a baseline (SSP2-45) with a modified specified stratospheric aerosol distribution using a repeated annual cycle of the WACCM-calculated Surface Area Density (SAD) for 2025 (the WACCM background before stratospheric aerosol injection was initiated) and with specified SSTs/sea-ice as in senD2-sai.\r\n\r\nThe senD2-sai simulation is based on the refD2 experiment but with a modified specified stratospheric aerosol distribution reflecting increased stratospheric aerosol amounts from stratospheric aerosol injection (SAI). Sea ice and sea surface temperatures (SSTs) are specified to follow a repeating annual cycle taken from those used by the same model for their refD2 experiment over 2020 - 2030, the period when SAI is assumed to have been initiated.\r\n\r\nThe refD2 experiment is the baseline projection for updated projections of ozone recovery. Specified forcings largely following the same specifications as for the SSP2-4.5 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6), with the exception of the near-surface mixing ratio of Ozone Depleting Substances which follow the baseline projection from WMO (2018).\r\n\r\nSSP2-4.5 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP2 with intermediate mitigation and adaptation challenges and climate forcing pathway RCP4.5 which leads to a radiative forcing of 4.5 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30" }, "onlineresource_set": [] }, { "ob_id": 43268, "uuid": "4cd869e07788412abdb90eb6409d1a06", "short_code": "result", "curationCategory": "", "dataPath": "/badc/ccmi/data/post-cmip6/ccmi-2022/ECCC/CMAM/senD2-ssp126", "numberOfFiles": 379, "volume": 118256883417, "fileFormat": "data are NetCDF formatted", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43267, "uuid": "48b7dc37e20b4d35bfb9bdcc794b6315", "short_code": "ob", "title": "CCMI-2022: senD2-ssp126 data produced by the CMAM model at CCCma", "abstract": "This dataset contains model data for CCMI-2022 experiment senD2-ssp126 produced by the CMAM model run by the modelling team at CCCma (Canadian Centre for Climate Modelling and Analysis) in the Climate Research Division of Environment and Climate Change Canada.\r\n\r\nExperiment senD2-ssp126 is a future projection with specified forcings largely following the same specifications as for the SSP1-2.6 scenario of the sixth phase of the Coupled Model Intercomparison Project (CMIP6). Ozone Depleting Substances (ODSs) are specified by the WMO(2018) baseline scenario.\r\n\r\nSSP1-2.6 is a Shared Socio-economic Pathway scenario that follows socio-economic storyline SSP1 with low climate change mitigation and adaptation challenges, and climate forcing pathway RCP2.6 which leads to a radiative forcing of 2.6 Wm-2 by the year 2100.\r\n\r\nWMO-2018 refers to the Scientific Assessment of Ozone Depletion: 2018.\r\n\r\nThe CCMI-2022 Chemistry-climate model initiative is a set of model experiments focused on the stratosphere, with the goals of providing updated projections towards the future evolution of the ozone layer and improving our understanding of chemistry-climate interactions from models.\r\n\r\n------------------------------------------\r\nSources of additional information\r\n------------------------------------------\r\nThe following web links are provided in the Details/Docs section of this catalogue record:\r\n- Review of the global models used within phase 1 of the Chemistry-Climate Model Initiative (CCMI)\r\n- A new set of Chemistry-Climate Model Initiative (CCMI) Community Simulations to Update the Assessment of Models and Support Upcoming Ozone Assessment Activities, David Plummer and Tatsuya Nagashima and Simone Tilmes and Alex Archibald and Gabriel Chiodo and Suvarna Fadnavis and Hella Garny and Beatrice Josse and Joowan Kim and Jean-Francois Lamarque and Olaf Morgenstern and Lee Murray and Clara Orbe and Amos Tai and Martyn Chipperfield and Bernd Funke and Martin Juckes and Doug Kinnison and Markus Kunze and Beiping Luo and Katja Matthes and Paul A. Newman and Charlotte Pascoe and Thomas Peter (2021), SPARC Newsletter, volume 57, pp 22-30" }, "onlineresource_set": [] }, { "ob_id": 43295, "uuid": "179b2d8273f64c65bd1e88389a129037", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/eocis/data/global_and_regional/SU_aerosol/ATSR2_AATSR/L3C/daily/v4.35.1/", "numberOfFiles": 6190, "volume": 7085013502, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43281, "uuid": "397b2da3a0d04bde8e5e1e341c829422", "short_code": "ob", "title": "Swansea University Aerosol Algorithm: (Advanced) Along-track Scanning Radiometers Daily Collated Level-3 Product v4.35.1", "abstract": "This dataset provides global Aerosol Optical Depth (AOD) from the Along-Track Scanning Radiometer-2 (ATSR-2) and Advanced Along-Track Scanning Radiometer (AATSR), presented on a 1° latitude-longitude grid, running from 1995-2003 (ATSR-2) and 2002-2012 (AATSR). \r\n\r\nThe product contains the daily mean and standard deviation of the total AOD and fine-mode AOD at 550nm retrieved within each grid cell. An uncertainty estimate is provided for the total AOD. It also includes a number of associated quantities determined consistently with the retrieval: AOD at 670nm, 870nm and 1600nm; surface reflectance at 550nm, 670nm, 870nm and 1600nm; Angstrom exponent, non-spherical dust AOD, absorbing AOD and single scattering albedos all at 550nm; and the cloud and land fractions. \r\n\r\nThe data here are part of a set of data that have been produced by Swansea University as part of the Earth Observation Climate Information Service (EOCIS) project, using algorithms developed under the European Space Agency’s (ESA) Climate Change Initiative for Aerosol project. The combined data collection covers the periods 1995-2012 and 2016 to present.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nPearson, K.J., North, P.R.J., Heckel, A., Hornero, A., Kinne, S., Popp, T., Sogacheva, L., Griesfeller, J., Atmospheric aerosol measurements from the ATSR-SLSTR series of dual-view satellite instruments 1995-2002. Scientific Data, 12, 410 https://doi.org/10.1038/s41597-025-04694-6" }, "onlineresource_set": [] }, { "ob_id": 43298, "uuid": "b943badfb661424b816fa296760523c1", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/eocis/data/global_and_regional/SU_aerosol/ATSR2_AATSR/L3C/monthly/v4.35.1/", "numberOfFiles": 212, "volume": 1316907343, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43282, "uuid": "f677ad3b44c24d5e8701153f14ab39e4", "short_code": "ob", "title": "Swansea University Aerosol Algorithm: (Advanced) Along-track Scanning Radiometers Monthly Collated Level-3 Product v4.35.1", "abstract": "This dataset provides global Aerosol Optical Depth (AOD) from the Along-Track Scanning Radiometer-2 (ATSR-2) and Advanced Along-Track Scanning Radiometer (AATSR), presented on a 1° latitude-longitude grid, running from 1995-2003 (ATSR-2) and 2002-2012 (AATSR). \r\n\r\nThe product contains the monthly mean and standard deviation of the total AOD and fine-mode AOD at 550nm retrieved within each grid cell. An uncertainty estimate is provided for the total AOD. It also includes a number of associated quantities determined consistently with the retrieval: AOD at 670nm, 870nm and 1600nm; surface reflectance at 550nm, 670nm, 870nm and 1600nm; Angstrom exponent, non-spherical dust AOD, absorbing AOD and single scattering albedos all at 550nm; and the cloud and land fractions. \r\n\r\nThe data here are part of a set of data that have been produced by Swansea University as part of the Earth Observation Climate Information Service (EOCIS) project, using algorithms developed under the European Space Agency’s (ESA) Climate Change Initiative for Aerosol project. The combined data collection covers the periods 1995-2012 and 2016 to present.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\nPearson, K.J., North, P.R.J., Heckel, A., Hornero, A., Kinne, S., Popp, T., Sogacheva, L., Griesfeller, J., Atmospheric aerosol measurements from the ATSR-SLSTR series of dual-view satellite instruments 1995-2002. Scientific Data, 12, 410 https://doi.org/10.1038/s41597-025-04694-6" }, "onlineresource_set": [] }, { "ob_id": 43300, "uuid": "db75a915c8344fd3a5c0fd961d211498", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/eocis/data/global_and_regional/SU_aerosol/SLSTR/L3C/daily/v1.14.1/", "numberOfFiles": 5668, "volume": 14437962058, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43283, "uuid": "f18f81e6fe014e5ab7b847f282f9de7b", "short_code": "ob", "title": "Swansea University Aerosol Algorithm: Sea and Land Surface Temperature Radiometers A and B Daily Collated Level-3 product v1.14.1", "abstract": "This dataset provides global Aerosol Optical Depth (AOD) from the Sea and Land Surface Temperature Radiometers (SLSTR), presented on a 1° latitude-longitude grid, starting in 2016. \r\n\r\nThe product contains the daily mean and standard deviation of the total AOD and fine-mode AOD at 550nm retrieved within each grid cell. An uncertainty estimate is provided for the total AOD. It also includes a number of associated quantities determined consistently with the retrieval: AOD at 670nm, 870nm and 1600nm and Angstrom exponent, non-spherical dust AOD, absorbing AOD and single scattering albedos all at 550nm. \r\n\r\nThe data here are part of a set of data that have been produced by Swansea University as part of the Earth Observation Climate Information Service (EOCIS) project, using algorithms developed under the European Space Agency’s (ESA) Climate Change Initiative for Aerosol project. The combined data collection covers the periods 1995-2012 and 2016 to present.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/\r\n \r\nWhen citing this dataset please also cite the associated data paper: \r\nPearson, K.J., North, P.R.J., Heckel, A., Hornero, A., Kinne, S., Popp, T., Sogacheva, L., Griesfeller, J., Atmospheric aerosol measurements from the ATSR-SLSTR series of dual-view satellite instruments 1995-2002. Scientific Data, 12, 410 https://doi.org/10.1038/s41597-025-04694-6" }, "onlineresource_set": [] }, { "ob_id": 43303, "uuid": "679a5b7ad838431093c47b7706b8ba2f", "short_code": "result", "curationCategory": "A", "dataPath": "/neodc/eocis/data/global_and_regional/SU_aerosol/SLSTR/L3C/monthly/v1.14.1/", "numberOfFiles": 191, "volume": 1053811918, "fileFormat": "Data are in NetCDF format", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43284, "uuid": "a89007aa668d4e2f940dbb3d3dfcc3dc", "short_code": "ob", "title": "Swansea University Aerosol Algorithm: Sea and Land Surface Temperature Radiometers A and B Monthly Collated Level-3 Product v1.14.1", "abstract": "This dataset provides global Aerosol Optical Depth (AOD) from the Sea and Land Surface Temperature Radiometers (SLSTR), presented on a 1° latitude-longitude grid, starting in 2016. \r\n\r\nThe product contains the monthly mean and standard deviation of the total AOD and fine-mode AOD at 550nm retrieved within each grid cell. An uncertainty estimate is provided for the total AOD. It also includes a number of associated quantities determined consistently with the retrieval: AOD at 670nm, 870nm and 1600nm and Angstrom exponent, non-spherical dust AOD, absorbing AOD and single scattering albedos all at 550nm. \r\n\r\nThe data here are part of a set of data that have been produced by Swansea University as part of the Earth Observation Climate Information Service (EOCIS) project, using algorithms developed under the European Space Agency’s (ESA) Climate Change Initiative for Aerosol project. The combined data collection covers the periods 1995-2012 and 2016 to present.\r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC BY 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper: \r\n\r\nPearson, K.J., North, P.R.J., Heckel, A., Hornero, A., Kinne, S., Popp, T., Sogacheva, L., Griesfeller, J., Atmospheric aerosol measurements from the ATSR-SLSTR series of dual-view satellite instruments 1995-2002. Scientific Data, 12, 410 https://doi.org/10.1038/s41597-025-04694-6" }, "onlineresource_set": [] }, { "ob_id": 43312, "uuid": "93794fbdf9b849c4a2eb8a676d50aa1a", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/deposits01/soc240571", "numberOfFiles": 16441, "volume": 76060542027, "fileFormat": "JPG, .txt, .csv", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43313, "uuid": "e2d0b06d869940029f3d7c1fae2a8b68", "short_code": "ob", "title": "Benthic images recorded by a Remotely Operated Vehicle stills camera during cruise JC241 in the Clarion-Clipperton Zone (Pacific Ocean, 2023)", "abstract": "A collection of benthic still images was obtained using a downward-looking camera mounted on the UK ISIS Remotely Operated Vehicle (ROV), deployed from the RRS James Cook during cruise JC241 in the abyssal plain (~4700 m depth) of the Clarion-Clipperton Zone, Pacific Ocean, in 2023. The Grasshopper2 GS2-GE-50S5C camera on the ROV collected vertically orientated still images at a target altitude of 2.5 m above the seabed, of which 16435 were suitable to assess benthic biological patterns around an area disturbed by a deep-sea mining machine in 1979. The mining machine (9 m wide, 14 m long, 4.5 m high) was operated by the Ocean Minerals Company (OMCO) from the ship Hughes Glomar Explorer on the seafloor between 15 and 18 March 1979 at a site centred 13°44'N 126°13.5'W. The images show the seabed covered by polymetallic nodules and the disturbance caused by the past mining activities. Images were collected within the Collection Tracks, in an adjacent area expected to have been impacted by a sediment plume (Plume Area), and a Control Area around 2 km away. The data were collected by scientists from the National Oceanography Centre, Southampton, UK as part of the NERC-funded Seabed Mining And Resilience To EXperimental impact (SMARTEX) project (NE/T003537/1)." }, "onlineresource_set": [] }, { "ob_id": 43314, "uuid": "242e5feeebad46fa98e2272332ea26da", "short_code": "result", "curationCategory": "", "dataPath": "/badc/deposited2024/Methane_Clumped_Database", "numberOfFiles": 3, "volume": 536745, "fileFormat": "BADC-CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43308, "uuid": "f90fc5b05bb4450f87e89b5f86038346", "short_code": "ob", "title": "Methane Clumped Isotopologues Database for POLYGRAM", "abstract": "The database was made as a part of POLYGRAM project (NE/V007149/1). The database aims to summarise the state of double substituted methane isotopologues (know also as clumped isotopologues, Δ13CH3D and Δ12CH2D2) measurement research, with an emphasis on compiling results of all relevant work and aid development of the inputs to atmospheric modelling studies. The compiled database comprises 1241 data records from 63 peer-reviewed articles. Database includes both field samples and laboratory experiments from numerous laboratories worldwide.\r\n\r\nThis database is made freely available to the scientific community. The database has a Digital Object Identifier (DOI). We rely on the ethics and integrity of the user to assure that the authors receive fair credit for their work. Users must include the citation of individual publication and following database citation in any publication or presentation using the product." }, "onlineresource_set": [] }, { "ob_id": 43316, "uuid": "9fdb4a5192124b4f8a21be74d6efac57", "short_code": "result", "curationCategory": "", "dataPath": "/neodc/esacci/fire/data/burned_area/SFDL/v1.0/pixel", "numberOfFiles": 323998, "volume": 3707585337793, "fileFormat": "geotiff", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43179, "uuid": "593397b5f9654d76b5d37761e7566ca6", "short_code": "ob", "title": "ESA Fire Climate Change Initiative (Fire_cci): Long-term Small Fire Dataset (SFDL) Burned Area pixel product for Test Sites: Amazonia, Africa and Siberia, version 1.0", "abstract": "The ESA Fire Disturbance Climate Change Initiative (Fire_cci) project aims to generate burned area developed from satellite observations. The Long-Term Small Fire Dataset (SFDL) pixel products have been obtained using spectral information from Landsat sensors for three study areas located in different parts of the world (Amazon, Sahel and Siberia), and coinciding with the ESA CCI High Resolution Land Cover product.\r\n\r\nThe dataset uses surface reflectance information from the Landsat-4 and Landsat-5 TM, Landsat-7 ETM+ and Landsat-8 OLI sensors, and covers the period 1990 to 2019, with a spatial resolution of 0.00025 degrees (approximately 30 m at the Equator)." }, "onlineresource_set": [] }, { "ob_id": 43317, "uuid": "2c2fdc22c9b642548aa5ef98cac4291a", "short_code": "result", "curationCategory": "A", "dataPath": "/bodc/deposits01/soc240656", "numberOfFiles": 1934, "volume": 3318795616, "fileFormat": "JPG, TXT, CSV", "storageStatus": "online", "storageLocation": "internal", "oldDataPath": [], "observation": { "ob_id": 43318, "uuid": "95e05ed31ee5492f9c38a56cfc58d9ec", "short_code": "ob", "title": "Benthic images recorded by a towed camera in 1978-79 in the Ocean Minerals Company (OMCO) area of the Clarion Clipperton Zone (Pacific Ocean)", "abstract": "In 1978 and 1979, three cruises were conducted on the R.V. Governor Ray. These expeditions collected still images of the seafloor of the Clarion Clipperton Zone in the Pacific Ocean using a towed camera system. This dataset includes the images collected in the vicinity of a deep-sea mining collector test carried out by the Ocean Minerals Company (OMCO) in 1979. Two cruises were carried out before the test (June 1978: GR7801; November 1978: GR7804) and one after (October 1979: GR7904). Monochrome images were collected using a Benthos 35 mm film camera, mounted vertically on a towed frame. Height above the seabed was determined with a Benthos Model 211 altimeter and recorded on each photographic frame. Images where seafloor was visible were collected at altitudes ranging from 0.6 - 9 m. Images were digitised from the original films. Only images collected at altitudes < 6 m were included in the dataset as these allowed reliable detection of megafaunal specimens > 20 mm. Overlapping images were removed through manual inspection (leaving a total of 1929 images). While the images did not image the tracks themselves, they provide important context about change in the baseline environment over time and are used to assess megafaunal communities. The still images deposited here form part of a larger set of images analysed in doi:10.1080/10641199309379903. The images here were used with data from a 2023 survey doi:10.5285/2392b266-126b-db3f-e063-7086abc0fe00, by scientists from the National Oceanography Centre, Southampton, UK as part of the NERC-funded Seabed Mining And Resilience To EXperimental impact (SMARTEX) project (NE/T003537/1)." }, "onlineresource_set": [] } ] }