Result List

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                "title": "FAAM C364 Instrument Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
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                "title": "Daily Transects and Areas from Light Detection and Ranging (LiDAR) scans of an eroding soft cliff at Happisburgh, UK (April-December 2019).",
                "abstract": "This dataset contains 236 transects and areas point-cloud elevation and colour intensity data collected daily along a 450 metre coastal stretch at Happisburgh, UK, over a time span of 9 months (April 6, 2019 to December 23, 2019). Included are subsets of these point-clouds, named transects and grids. Scans were taken approximately daily, and on some days only one scanner was run resulting in half-size scans. A single FARO S350 LiDAR scanner was placed at two fixed locations on the beach, spaced 178 metres alongshore and between 30 to 40 metres from the 10 metre high cliff. The duration of the scanning at each location was around 30 minutes. These data were collected to better understand the dynamic of beach-cliff and shore platform interaction along soft cliffed coasts. The shapefiles of the transects and areas are also included for a more complete description. ScanLAB Projects Ltd were responsible for the collection of the data, along with the British Geological Survey (BGS), funded by the UK Natural Environment Research Council (NE/M004996/1; BLUE-coast project)."
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                "title": "Ozone Abundance Data from Teledyne T400 Instrument at London Honor Oak Park Air Quality Supersite",
                "abstract": "Mass Fraction of Ozone (O3) in air measured at 5 metres above ground level by a Teledyne T400 Ozone Analyzer Instrument at the London Honor Oak Park Air Quality Site (LHOP) for the Integrated Research Observation System for Clean Air (OSCA) project, 2019 onwards."
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                "title": "ESA Lakes Climate Change Initiative (Lakes_cci):  Lake products, Version 2.1",
                "abstract": "This dataset contains the Lakes Essential Climate Variable, which is comprised of processed satellite observations at the global scale, over the period 1992-2022, for over 2000 inland water bodies. This dataset was produced by the European Space Agency (ESA) Lakes Climate Change Initiative (Lakes_cci) project. For more information about the Lakes_cci please visit the project website. \r\n\r\nThis is version 2.1.0 of the dataset.\r\n\r\nThe six thematic climate variables included in this dataset are:\r\n• Lake Water Level (LWL), derived from satellite altimetry, is fundamental to understand the balance between water inputs and water loss and their connection with regional and global climate change.\r\n• Lake Water Extent (LWE), modelled from the relation between LWL and high-resolution spatial extent observed at set time-points, describes the areal extent of the water body. This allows the observation of drought in arid environments, expansion in high Asia, or impact of large-scale atmospheric oscillations on lakes in tropical regions for example. .\r\n• Lake Surface Water temperature (LSWT), derived from optical and thermal satellite observations, is correlated with regional air temperatures and is informative about vertical mixing regimes, driving biogeochemical cycling and seasonality.\r\n• Lake Ice Cover (LIC), determined from optical observations, describes the freeze-up in autumn and break-up of ice in spring, which are proxies for gradually changing climate patterns and seasonality.\r\n• Lake Water-Leaving Reflectance (LWLR), derived from optical satellite observations, is a direct indicator of biogeochemical processes and habitats in the visible part of the water column (e.g. seasonal phytoplankton biomass fluctuations), and an indicator of the frequency of extreme events (peak terrestrial run-off, changing mixing conditions).\r\n• Lake Ice Thickness (LIT), containing LIT information over Great Slave lake from 2002-2022.\r\n\r\nData generated in the Lakes_cci are derived from multiple satellite sensors including: TOPEX/Poseidon, Jason, ENVISAT, SARAL, Sentinel 2-3, Landsat 4, 5, 7 and 8, ERS-1, ERS-2, Terra/Aqua and Metop-A/B.\r\n\r\nSatellite sensors associated with the thematic climate variables are as follows:\r\nLWL: TOPEX/Poseidon, Jason-1, Jason-2, Jason-3, Sentinel-6A, Envisat RA/RA-2, SARAL AltiKa, GFO, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 RA, ERS-2; \r\nLWE: Landsat 4 TM, 5 TM, 7 ETM+, 8 OLI, Sentinel-1 C-band SAR, Sentinel-2 MSI, Sentinel-3A SRAL, Sentinel-3B SRAL, ERS-1 AMI, ERS-2 AMI;\r\nLSWT: Envisat AATSR, Terra/Aqua MODIS, Sentinel-3A ATTSR-2, Sentinel-3B, ERS-2 AVHRR, Metop-A/B; \r\nLIC: Terra/Aqua MODIS; \r\nLWLR: Envisat MERIS, Sentinel-3A OLCI A/B, Aqua MODIS;\r\nLIT: Jason1, Jason2, Jason3, POSEIDON-2, POSEIDON-3 and POSEIDON-3B.\r\n\r\nDetailed information about the generation and validation of this dataset is available from the Lakes_cci documentation available on the project website  and in Carrea, L., Crétaux, JF., Liu, X. et al. Satellite-derived multivariate world-wide lake physical variable timeseries for climate studies. Sci Data 10, 30 (2023). https://doi.org/10.1038/s41597-022-01889-z"
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci): Northern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the Northern Hemisphere polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Data are only available for the NH winter months, October - April."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the ENVISAT satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area grid for the period October 2002 to March 2012. Data is only available for the NH winter months, October - April."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the CryoSat-2 satellite on a monthly grid (L3C), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data gridded on a Lambeth Azimuthal Equal Area grid for the period November 2010 to April 2020. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from the Envisat satellite on a monthly grid (L3C), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite at Level 3C (L3C). This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides monthly gridded sea ice thickness data on a Lambeth Azimuthal Equal Area Projection for the period October 2002 to March 2012. Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from CryoSat-2   on the satellite swath (L2P), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the NH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite.   This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data for the months October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Northern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the northern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data for the winter months of October to April annually on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from CryoSat-2 on the satellite swath (L2P), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the SH polar region, derived from the SIRAL (SAR Interferometer Radar ALtimeter) instrument on the CryoSat-2 satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period November 2010 to April 2020.  Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly consider the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information."
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                "title": "ESA Sea Ice Climate Change Initiative (Sea_Ice_cci):  Southern hemisphere sea ice thickness from Envisat on the satellite swath (L2P), v3.0",
                "abstract": "This dataset provides a Climate Data Record of Sea Ice Thickness for the southern hemisphere polar region, derived from the RA-2 (Radar Altimeter -2) instrument on the Envisat satellite. This product was generated in the context of the ESA Climate Change Initiative Programme (ESA CCI) by the Sea Ice CCI (Sea_Ice_cci) project.\r\n\r\nIt provides daily sea ice thickness data on the satellite measurement grid (Level 2P) at the full sensor resolution for the period October 2002 to March 2012.   Note, the southern hemisphere sea ice thickness dataset is an experimental climate data record, as the algorithm does not properly considers the impact of the complex snow morphology in the freeboard retrieval. Sea ice thickness is provided for all months but needs to be considered biased high in areas with high snow depth and during the southern summer months. Please consult the Product User Guide (PUG) for more information."
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                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Climatology product, version 3.0",
                "abstract": "This dataset provides daily climatological mean sea surface temperature (SST) on a global 0.05° latitude-longitude grid, derived from the SST CCI analysis data for the period 1991 to 2020 (30 years).  \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative (CCI) Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.2 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n* Improved retrieval with respect to desert-dust aerosols \r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
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                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Level 4 Analysis product, version 3.0",
                "abstract": "This dataset provides daily-mean sea surface temperatures (SST), presented on global 0.05° latitude-longitude grid, spanning 1980 to present.     This is a Level 4 product, with gaps between available daily observations filled by statistical means.\r\n\r\nThe SST CCI Analysis product contains estimates of daily mean SST and sea ice concentration. Each SST value has an associated uncertainty estimate.  \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.  \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of dual-view SLSTR data from 2016 onwards \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\n* Inclusion of L2P passive microwave AMSR data \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41573,
            "uuid": "b1b27056fc3e4933998a0196abc6228a",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AMSR/L2P/v3.0.1/",
            "numberOfFiles": 154471,
            "volume": 419983429851,
            "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 40859,
                "uuid": "15a170dad3064fefa8936bd50877a93e",
                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Microwave Scanning Radiometer (AMSR) Level 2 Pre-processed (L2P) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Microwave Scanning Radiometers (AMSR), presented on the native geometry of observation (Level 2), and spanning 2002 to 2017. \r\n\r\nThe SST CCI AMSR product contains two different SST estimates. The first is the subskin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. Additionally, the AMSR files contain a satellite estimate of the surface wind speed. \r\n \r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41581,
            "uuid": "a0aedd74914546faa62d131d3bd913a9",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/ice_sheets_greenland/data/greenland_ML_calving_front_locations/v1.0",
            "numberOfFiles": 16,
            "volume": 3146980,
            "fileFormat": "txt, geojson, png",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 39549,
                "uuid": "35a3e7e5ad2946859ac31c36605486f0",
                "short_code": "ob",
                "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Machine Learning Generated Greenland Calving Front Locations v1.0",
                "abstract": "Calving Front locations for Upernavik A,E,F, Humboldt and Hagen glaciers in Greenland, generated by a deep learning based model using Sentinel-2 imagery.\r\n\r\nThe calving front location is generated by a deep learning based model using Sentinel-2 imagery acquired from 2019-2020. The digitized calving fronts are stored in geoJSON vector file format and include metadata information on the sensor and processing steps in the corresponding attribute table.\r\n\r\nThe CCI Calving Front Locations (CFL) v1.0 release contains one primary dataset, the calving front locations, and auxiliary files to describe the file product: locations.png and glaciers.geojson for visualizing the glaciers, README and DESCRIPTION text files about the product structure, and a visual example of what a calving front looks like. The Greenland CCI Calving Front Locations (CFL) v1.0 product is an experimental product using deep learning to automatically derive calving front locations for selected glaciers based on Sentinel-2 imagery at the end of the summer season.\r\n\r\nThe product was generated by S[&]T Norway and ENVEO."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41582,
            "uuid": "83ccc3ec5f5a47bc8059fdb25ee859cd",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2023/wind_driven_rain",
            "numberOfFiles": 40,
            "volume": 1049280170,
            "fileFormat": "The data are provided in NetCDF.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41421,
                "uuid": "3acecae819b84507ad4d62f87cf35155",
                "short_code": "ob",
                "title": "Met Office Wind-Driven Rain (WDR)",
                "abstract": "This dataset contains the annual index of wind-driven rain (sum of all wind-driven rain spells in each year) derived from the UK Climate Projections (UKCP18) for a range of future global warming levels provided on a 5 km British National Grid (BNG). The annual index is calculated for eight wall orientations corresponding to the cardinal and ordinal points of the compass. \r\n\r\nWind-driven rain occurs when falling rain is blown by a horizontal wind so that it falls diagonally towards the ground. The annual index of wind-driven rain is the sum of all wind-driven rain spells for a given wall orientation and time period. It’s measured as the volume of rain blown from a given direction in the absence of any obstructions, with units of litres per square metre per year.\r\n\r\nWind-driven rain is calculated from hourly weather and climate data using an industry-standard formula from ISO 15927–3:2009, which is based on the product of wind speed and rainfall totals. Wind-driven rain is only calculated if the wind would strike a given wall orientation. A wind-driven rain spell is defined as a wet period separated by at least 96 hours with little or no rain (below a threshold of 0.001 litres per m2 per hour).\r\n\r\nThe annual index of wind-driven rain is calculated for a baseline (historical) period of 1981-2000 (corresponding to 0.61°C warming) and for global warming levels of 2.0°C and 4.0°C above the pre-industrial period (defined as 1850-1900). The warming between the pre-industrial period and baseline is the average value from six datasets of global mean temperatures available on the Met Office Climate Dashboard: https://climate.metoffice.cloud/dashboard.html.\r\n\r\nThe magnitudes of 1 in 3 year wind-driven rain spells (i.e. wet spells that would be expected to occur, on average, once every three years) are used to divide the UK into four zones in Approved Document C of the buildings regulations. The magnitudes of 1 in 3 year wind-driven rain spells were calculated for the baseline period (1981-2000) and 20-year periods corresponding to 2°C and 4°C of warming. The magnitudes of all wet spells (here, sum of hourly values of the wind-driven rain metric, I) were calculated, and the largest wet spell in each year was found (in the accompanying report, the magnitude of a wet spell is given the symbol Is' [\"Is prime\"] and has units of litres per metre-squared per spell). For each time period, the largest spells in all years and ensemble members were pooled together. A Gumbel distribution was fitted to the pooled data and used to estimate the magnitude of the 1 in 3 year wet spells across the UK.\r\n\r\nWind-driven rain is required for buildings standards. It is a major source of moisture in walls. Areas subject to very high levels of wind-driven rain may not be suitable for cavity-wall insulation. Under certain circumstances, cavity-wall insulation can enhance the transfer of moisture through walls to the inside of a building causing mould and damp problems."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41584,
            "uuid": "6dea05d435684e18be0fd3916b4d014a",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/name_nwp/data/uk/UM1p5km_Mk4/",
            "numberOfFiles": 1877760,
            "volume": 14646483086168,
            "fileFormat": "Data are in packed PP format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 41589,
            "uuid": "11287070a396416a83b9db55cc8e971e",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/name_nwp/data/uk/UM1p5km_Mk4/",
            "numberOfFiles": 2105105,
            "volume": 16424066052754,
            "fileFormat": "Data are in packed PP format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41588,
                "uuid": "a1e5dd132fad4e129669e71adeed1ab1",
                "short_code": "ob",
                "title": "UK 1.5km NWP meteorological data for Met Office NAME dispersion model (Mk4: Jul 2017 - current)",
                "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": 41593,
            "uuid": "1a68e68b666a4310957dcccbc89fa371",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/name_nwp/data/global/UMG_Mk11/",
            "numberOfFiles": 225359,
            "volume": 19498881006381,
            "fileFormat": "Data are in packed PP format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41592,
                "uuid": "45cb520616fc499c80aefd0b356a81f5",
                "short_code": "ob",
                "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk11: Apr 2022 - current)",
                "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the 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. 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 3-hourly resolution. 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 global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41599,
            "uuid": "272cfd9498e8404faf41c9887b00d02c",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/name_nwp/data/global/UMG_Mk10/",
            "numberOfFiles": 402907,
            "volume": 34766040201496,
            "fileFormat": "Data are in packed PP format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41598,
                "uuid": "7d0fff9f59b94a3da347e3ae10bd8fc1",
                "short_code": "ob",
                "title": "Global NWP meteorological data for Met Office NAME dispersion model (Mk10: June 2017 - May 2022)",
                "abstract": "This dataset contains Numerical Weather Prediction (NWP) global meteorological data produced by the Met Office Unified Model. The files in the 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. 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 3-hourly resolution. 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 global gridded NWP meteorological data is generically useful for a whole range of scientific research and applications."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41602,
            "uuid": "85c43349cb944b488f07636d85a9cebc",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/hadcm3/data/PRECIS",
            "numberOfFiles": 3104366,
            "volume": 5173959890830,
            "fileFormat": "Data are PP formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41601,
                "uuid": "5725de8c7e934a05b978ff2b93e4438f",
                "short_code": "ob",
                "title": "HadCM3Q PRECIS data generated for the QUMP (Quantifying Uncertainty in Model Predictions) Project using IPCC's SRES A1B future emissions scenario.",
                "abstract": "This PRESCIS dataset (Providing REgional Climates for Impacts Studies) is output data from the HadCM3Q GCM ensemble, which used the SRES A1B scenario (see the Special Report on Emissions Scenarios (SRES) www.ipcc.ch/pdf/special-reports/spm/sres-en.pdf) spanning December 1949 to November 2099. The data are split up according to their runid and then by their stash code. \r\n\r\nThe Quantifying Uncertainty in Model Predictions (QUMP) research theme aims to provide probabilistic predictions of future climate. These are based on large ensembles of simulations of equilibrium and time-dependent change, carried out by perturbing poorly constrained parameters controlling key physical and biogeochemical processes in the HadCM3 coupled ocean-atmosphere global climate model. Further ensembles of regional climate simulations at high resolution, driven by boundary conditions obtained from the HadCM3 ensemble, allow the specification of probabilistic predictions at spatial scales suitable for climate impact studies. These experiments allow quantification of the effects of earth system modelling uncertainties and internal climate variability on feedbacks likely to exert a significant influence on twenty-first century regional climate. \r\n \r\nThe diagnostics are either on the horizontal 'pressure grid' or the horizontal 'wind grid'. For the 'wind grid' an Arakawa B grid layout is used, in which wind variables are offset from all other variables (said to be on the 'pressure grid') by half a grid box in both directions. The 'wind grid' has the same number of points in the east-west direction as the 'pressure grid', but one less in the north-south direction.\r\n\r\nSome files are available as monthly means and some are available as daily means.  \r\n\r\nEach daily mean file contains 360 fields (one field per day for a year in the 360 day calendar used by HadCM3Q). Each monthly mean tarball contains files with one field (one field for a month covering the span of December 1949 to November 2099).\r\n\r\nTables of information about the runids and stash codes can be found in the PRECIS_Data_Readme stored alongside the fileset."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41615,
            "uuid": "dfe6c9991eb247cf94e707f2a9753bc0",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/vision/data/Vision_UKESM1_hourly_ozone",
            "numberOfFiles": 15234,
            "volume": 132012087023,
            "fileFormat": "pp",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41547,
                "uuid": "300046500aeb4af080337ff86ae8e776",
                "short_code": "ob",
                "title": "VISION: UKESM1 hourly modelled ozone for comparison to observations",
                "abstract": "Two UK Earth System Model (UKESM1) hindcasts have been performed in support of the Virtual Integration of Satellite and In-situ Observation Networks (VISION) project (NE/Z503393/1).\r\n\r\nData is provided as raw model output in Met Office PP (32-bit) format that can be read by the Iris (https://scitools-iris.readthedocs.io/en/stable/) or cf-python (https://ncas-cms.github.io/cf-python/) libraries.\r\n\r\nThis is global data at N96 L85 resolution (1.875 x 1.25, 85 model levels up to 85km). Simulations were performed on the Monsoon2 High Performance Computer (HPC).\r\n\r\nThe first dataset (Jan 1982 to May 2022) contains hourly ozone concentrations on the lowest model level (20m above the surface).  \r\n\r\nThe second dataset (Jan 2010 to Dec 2020) contains hourly ozone concentrations and hourly Heaviside function on 37 fixed pressure levels. Data is only provided for days in which ozone was measured by the FAAM aircraft (for comparison purposes). \r\n\r\nOzone data is provided in mass mixing ratio (kg species/kg air)."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41616,
            "uuid": "76ed8f0fc7764c25ba281895eeb045f5",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/vision/data/Vision_FAAM_ozone_cf_compliant_2010_2020",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "NetCDF",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 41619,
            "uuid": "da419117135c4dfca3288bb1d0cebfef",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/aerosol/data/ATSR2_SU/L3/v4.3/",
            "numberOfFiles": 2754,
            "volume": 2658200600,
            "fileFormat": "Data are in NetCDF format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [
                {
                    "ob_id": 31803,
                    "uuid": "6c49a5a5b4354a7bb5b080b7c735f222",
                    "short_code": "result",
                    "title": null,
                    "abstract": null
                }
            ],
            "observation": {
                "ob_id": 27681,
                "uuid": "39909dc233b34118a80dd6fa8a7af553",
                "short_code": "ob",
                "title": "ESA Aerosol Climate Change Initiative (Aerosol_cci): Level 3 aerosol products from ATSR-2 (SU algorithm), Version 4.3",
                "abstract": "The ESA Climate Change Initiative Aerosol project has produced a number of global aerosol Essential Climate Variable (ECV) products from a set of European satellite instruments with different characteristics.  This dataset comprises the Level 3 daily and monthly aerosol products from the ATSR-2 instrument on the ERS-2 satellite, using the Swansea University (SU) algorithm, version 4.3.    Data cover the period 1995 - 2003.\r\n\r\nFor further details about these data products please see the documentation."
            },
            "onlineresource_set": []
        },
        {
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            "uuid": "f59176acbd6d4373984787cb3a349f53",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_EXSCALABAR_LABdata/",
            "numberOfFiles": 13,
            "volume": 11300447,
            "fileFormat": "Data are BADC-CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41347,
                "uuid": "8cd84a935fdf4e28b8f42b95ebeee657",
                "short_code": "ob",
                "title": "EXSCALABAR optical data from SASSO project - Version 0",
                "abstract": "This dataset contains data from the University of Manchester EXtinction, SCattering and Absorption of Light for AirBorne Aerosol Research (EXSCALABAR) collected at the University of Exeter, between May and June 2019, and at the University of Manchester, between January 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. EXSCALABAR is a custom-built aerosol optical spectroscopy instrument. In this study, it was used to measure the absorption and extinction coefficients of particles at 405 nm and 660 nm. These data were then used to derive the refractive index of the aerosols.\r\nThis version 0 dataset contains light absorption/extinction coefficient at multiple wavelengths. The EXSCALABAR is operated by Met office scientists."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41625,
            "uuid": "41312e974fa645ee82308a7312b6eb72",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_GAS_LABdata",
            "numberOfFiles": 5,
            "volume": 440773,
            "fileFormat": "Data are BADC-CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 41352,
                "uuid": "3fa0152b55fd4a3b8357fdfcfc55f45d",
                "short_code": "ob",
                "title": "NOx and O3 gas data from SASSO project - Version 0",
                "abstract": "This dataset contains data from the University of Manchester nitrogen oxides (NOx) monitor and ozone (O3) monitor collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. The NOx and O3 concentrations inside the chamber were measured in real time during the experiment. \r\n\r\nNO, NO2, and NOx concentrations were measured using the NOx analyzer (Model 42i, Thermo Scientific), while O3 concentration was measured using the O3 analyzer (Model 49C, Thermo Scientific). These data reveal the conditions for each experiment.\r\n\r\nThis version 0 dataset contains NO, NO2, NOx and O3 concentration. The NOx monitor and O3 monitor are operated by University of Manchester scientists."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41626,
            "uuid": "419b9d6c4b174cb79ec4931b1bc37e7f",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_SMPS_LABdata",
            "numberOfFiles": 0,
            "volume": 0,
            "fileFormat": "Data are in BADC-CSV format",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": null,
            "onlineresource_set": []
        },
        {
            "ob_id": 41627,
            "uuid": "fb49430159b444dab565b86555516c64",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/SASSO/SASSO_MAN_SP2_LABdata",
            "numberOfFiles": 11,
            "volume": 10066392,
            "fileFormat": "Data are BADC-CSV formatted",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
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                "ob_id": 41345,
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                "short_code": "ob",
                "title": "Single Particle Soot Photometer (SP2) data from SASSO project - Version 0",
                "abstract": "This dataset contains number and mass concentration data of refractory black carbon (rBC) from the University of Manchester Single Particle Soot Photometer (SP2). Data were collected at the University of Exeter wildFIRE lab, between May and June 2019, and at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. These data were used to derive the refractive index of black carbon aerosols.\r\nThis version 0 dataset contains black carbon concentrations. The SP2 instrument (Droplet Measurement Technologies, Colorado, USA) is operated by University of Manchester scientists."
            },
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            "observation": {
                "ob_id": 41353,
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                "short_code": "ob",
                "title": "Scanning Mobility Particle Sizer (SMPS) data from SASSO project - Version 0",
                "abstract": "This dataset contains number size distribution of monodisperse secondary organic aerosols (SOA) data from the University of Manchester Scanning Mobility Particle Sizer (SMPS) collected at the University of Manchester, between January and February 2020 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. These data were used to derive the refractive index of the SOA.\r\nThis version 0 dataset contains particle size distribution. The SMPS is operated by University of Manchester scientists."
            },
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            "observation": {
                "ob_id": 41350,
                "uuid": "180b9636108a46c99611a288f60bb894",
                "short_code": "ob",
                "title": "Aerosol Mass Spectrometer (AMS) data from SASSO project- Version 0",
                "abstract": "This dataset contains real-time measurements organic aerosol mass concentration data from the University of Manchester Aerosol Mass Spectrometer (AMS). These data were collected at the University of Exeter, between May and June 2019 as part of the Soot Aerodynamic Size Selection for Optical properties (SASSO) project. Combined with the black carbon mass concentration measured by the Single Particle Soot Photometer (SP2), this information can be used to identify the burning phase (pyrolysis, flaming, or smouldering) in wood combustion experiments.\r\nThis version 0 dataset contains organic mass concentration. The AMS is operated by University of Manchester scientists."
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            "dataPath": "/badc/deposited2024/SASSO/SASSO_UMDATA_JOHNSON_DAMANY-PE",
            "numberOfFiles": 241,
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                "ob_id": 41531,
                "uuid": "025b174a38404e8bbc69ef7f144e577d",
                "short_code": "ob",
                "title": "SASSO data from the Met Office Unified Model",
                "abstract": "This dataset is the Met Office Unified Model data that was used in the SASSO-funded study:\r\nDamany-Pearce, L., Johnson, B., Wells, A. et al. Australian wildfires cause the largest stratospheric warming since Pinatubo and extends the lifetime of the Antarctic ozone hole. Sci Rep 12, 12665 (2022). https://doi.org/10.1038/s41598-022-15794-3\r\n\r\nAs part of the SASSO project researchers collaborated with the Met Office and used the UK Earth System Model (UKESM1) to investigate the impact of the intense wildfires and resulting smoke injections in the stratosphere. The work concluded that: Australian wildfires cause the largest stratospheric warming since Pinatubo and extended the lifetime of the Antarctic ozone hole. Data provided here is from the UKESM1 simulations used in those investigations.\r\n\r\nSpecifically the data consists of zonal mean air temperature (T) and horizonal wind (U) as monthly means. These are monthly means from a single year, since the simulations in the study ran just for a one year. In the dataset there are 3 groups of simulations, each with its own folder:   \"aerosol_and_ozone\",  \"aerosol_only\" and  \"ozone_only\". In each group there are 10 simulations making up a 10 member ensemble. The zonal means are the average across all longitudes (-180 to 180)."
            },
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        {
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/vision/data/Vision_FAAM_ozone_cf_compliant_2010_2020",
            "numberOfFiles": 553,
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            "observation": {
                "ob_id": 41549,
                "uuid": "8df2e81dbfc2499983aa87781fb3fd5a",
                "short_code": "ob",
                "title": "VISION: Collated subset of FAAM ozone data 2010 to 2020",
                "abstract": "This is a subset extacted from the FAAM dataset containing ozone measurements made on board the FAAM aircraft using the TECO 49 UV photometric ozone instrument between 2010 and 2020. \r\nThis dataset was compiled to facilitate data access and integration for the Virtual Integration of Satellite and In-situ Observation Networks (VISION) project (NE/Z503393/1)\r\n\r\nData for all flights were extracted from the core FAAM data and new files were created which have the following advantages:\r\n- consistent variable names across the whole time period. \r\n- data points with missing coordinates, or ozone being flagged as inadequate, have been removed.\r\n- files are fully CF-compliant.\r\n- files contain geospatial coordinates and ozone\r\n- filenames contain flight date, unique flight identifier and name of the measurement campaign associated to the flight.\r\n\r\nThis dataset is a subset of the Facility for Airborne Atmospheric Measurements (FAAM) flights dataset collection https://catalogue.ceda.ac.uk/uuid/affe775e8d8890a4556aec5bc4e0b45c"
            },
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        {
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            "short_code": "result",
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            "dataPath": "/neodc/aatsr_multimission/atsr1-v3.0.1/data/at1_ar__2p",
            "numberOfFiles": 73433,
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                "short_code": "ob",
                "title": "ATSR-1: Average Surface Temperature (AST) Product (AT1_AR__2P), v3.0.1",
                "abstract": "Along-Track Scanning Radiometer (ATSR) 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\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Average Surface Temperature (AST) Product. These data are the Level 2 spatially averaged geophysical product derived from Level 1B product and auxiliary data.  This data is from the 3rd reprocessing and tagged v3.0.1\r\n\r\nThere are two types of averages provided: 10 arcminute cells and 30 arcminute cells. All cells are present regardless of the surface type. Hence, the sea (land) cells would also have the land (sea) records even though these would be empty. Cells containing coastlines will have both valid land and sea records; the land (sea) record only contains averages from the land (sea) pixels. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files."
            },
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        {
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/aatsr_multimission/atsr1-v3.0.1/data/at1_ast_bp",
            "numberOfFiles": 1,
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            "fileFormat": "ENVISAT PDS",
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            "observation": {
                "ob_id": 41639,
                "uuid": "484dcbe7fe0a49c2983740ae96dd1d83",
                "short_code": "ob",
                "title": "ATSR-1: Three Band Colour Composite Browse Product (AT1_AST_BP) v3.0.1",
                "abstract": "Along-Track Scanning Radiometer (ATSR) 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\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Browse Product. These data are 3 band colour composite, quick-look images at coarse resolution. The browse product for the ATSR-1 and the ATSR-2 missions were introduced during the third reprocessing of data products. This product was derived from the Level 1B product and auxiliary data."
            },
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        {
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            "short_code": "result",
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            "dataPath": "/neodc/aatsr_multimission/atsr1-v3.0.1/data/at1_met_2p",
            "numberOfFiles": 1,
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            "fileFormat": "ENVISAT PDS",
            "storageStatus": "online",
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            "observation": {
                "ob_id": 41640,
                "uuid": "c0696748b0fb43058d68ecf284f037ed",
                "short_code": "ob",
                "title": "ATSR-1: Sea Surface Temperature Meteo Product (AT1_MET_2P) v3.0.1",
                "abstract": "Along-Track Scanning Radiometer (ATSR) 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\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Spatially Averaged Sea Surface Temperature Product for Meteo Users. These data are the Level 2 product designed for the use by meteorological offices derived from Level 2 AST product.\r\n\r\nThe product contains only the sea surface temperature with spatial resolution of 10 arc minutes. It also contains the Average Brightness Temperature (ABT) fields, which includes brightness temperature and TOA sea record on the same spatial resolution. Like the AST product this product is derived from, all areas contains data, where the land pixels have empty data, and the coasts containing averages derived only from the sea pixels in the cell. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files."
            },
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        {
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            "uuid": "d72ededb20734dde8cb46f4de2e21a6d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/aatsr_multimission/atsr1-v3.0.1/data/at1_toa_1p",
            "numberOfFiles": 129407,
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            "observation": {
                "ob_id": 41638,
                "uuid": "b13fe2c503d34d6297440de27af5ed90",
                "short_code": "ob",
                "title": "ATSR-1: Gridded Brightness Temperature/Reflectance (GBTR) product (AT1_TOA_1P), v3.0.1",
                "abstract": "Along-Track Scanning Radiometer (ATSR) 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\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Gridded Brightness Temperature/Reflectance (GBTR) Product. These data are the Level 1B product that consists of Top of Atmosphere (TOA) radiance measurements and brightness temperatures at full resolution for both the nadir and forward views. \r\n\r\nThe product was calibrated for instrumental and atmospheric effects and re-sampled to a fixed grid aligned to the sub-satellite track. This product were derived from the Level 0 product and auxiliary data, and serves as the input data for all Level 2 products. The third reprocessing was done to implement the updated algorithms, processors, and auxiliary files."
            },
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        {
            "ob_id": 41645,
            "uuid": "fcbce7e40967407fb260ad97c98ac11d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/aatsr_multimission/atsr1-v3.0.1/data/at1_nr__2p",
            "numberOfFiles": 74012,
            "volume": 3098288453247,
            "fileFormat": "ENVISAT PDS",
            "storageStatus": "online",
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            "observation": {
                "ob_id": 41637,
                "uuid": "122a21a0ffe14051b4b6eed752480c36",
                "short_code": "ob",
                "title": "ATSR-1: Gridded Surface Temperature (GST) Product (AT1_NR__2P), v3.0.1",
                "abstract": "Along-Track Scanning Radiometer (ATSR) 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\nThis dataset contains the Along-Track Scanning Radiometer on ESA ERS-1 satellite (ATSR-1) Gridded Surface Temperature (GST) Product. These data are the Level 2 full spatial resolution (approximately 1 km by 1 km) geophysical product derived from Level 1B product and auxiliary data. \r\n\r\nThe contents of the pixel fields, which are a mixture of Top of Atmosphere (TOA) and surface brightness temperature/radiance, are switch-able depending on the surface type. The third reprocessing was done to implement updated algorithms, processors, and auxiliary files."
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41646,
            "uuid": "e7174e2c2a6346b88d040d1e077c162e",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L3C/v3.0/",
            "numberOfFiles": 10179,
            "volume": 292308422299,
            "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification.",
            "storageStatus": "online",
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            "oldDataPath": [],
            "observation": {
                "ob_id": 40865,
                "uuid": "a104ed92bddd4c56b11127d4cc49b8d4",
                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 3 Collated (L3C) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), daily collations on a 0.05° latitude-longitude grid, spanning 2016 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n \r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research.  \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS).  \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41647,
            "uuid": "159c4759788746b3a2eefda2e7ba9aa4",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L3U/v3.0/",
            "numberOfFiles": 1206330,
            "volume": 986265904667,
            "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 40864,
                "uuid": "61b7a51d72b54692890d45818307d72f",
                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 3 Uncollated (L3U) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), presented on a 0.05° latitude-longitude grid, and spanning 2016 to 2021. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41651,
            "uuid": "c5db7fbbd23b4b22a7a5a45824b65734",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/SLSTR/L2P/v3.0/",
            "numberOfFiles": 1208393,
            "volume": 5489899353001,
            "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification.",
            "storageStatus": "online",
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            "observation": {
                "ob_id": 40863,
                "uuid": "f4151599eb7b491c9f4ce75489eb8b1e",
                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Sea and Land Surface Temperature Radiometer (SLSTR) Level 2 Pre-processed (L2P) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Sea and Land Surface Temperature Radiometers (SLSTR), presented on the native geometry of observation, and spanning 2016 to 2021. \r\n\r\nThe SST CCI SLSTR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41652,
            "uuid": "bd806c9860c24e90945fe568601302d9",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/neodc/eocis/data/global_and_regional/sea_surface_temperature/CDR_v3/AVHRR/L3C/v3.0/",
            "numberOfFiles": 72355,
            "volume": 4850214203266,
            "fileFormat": "netCDF-4 classic following: Climate and Forecast (CF) metadata conventions, GHRSST Data Specification 2.0, and SST CCI Product Specification.",
            "storageStatus": "online",
            "storageLocation": "internal",
            "oldDataPath": [],
            "observation": {
                "ob_id": 40862,
                "uuid": "be418645dfa542df86165a7caad24284",
                "short_code": "ob",
                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Collated (L3C) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), daily collations on a 0.05° latitude-longitude grid, spanning 1980 to present, and separated into daytime and night-time files. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nData from 2022 onwards are provided as an Interim Climate Data Record (ICDR) and will be updated daily at one month behind present. The Copernicus Climate Change Service (C3S) funded the development of the ICDR extension and production of the ICDR during 2022. From 2023 onwards the production of the ICDR is funded by the UK Earth Observation Climate Information Service (EOCIS) and Marine and Climate Advisory Service (MCAS). \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
            },
            "onlineresource_set": []
        },
        {
            "ob_id": 41653,
            "uuid": "590717de1336470f93559bac19cab4f2",
            "short_code": "result",
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                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 3 Uncollated (L3U) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on a 0.05° latitude-longitude grid, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data)  \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
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                "ob_id": 40860,
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                "title": "ESA Sea Surface Temperature Climate Change Initiative (SST_cci): Advanced Very High Resolution Radiometer (AVHRR) Level 2 Pre-processed (L2P) product, version 3.0",
                "abstract": "This dataset provides global sea surface temperatures (SST) from Advanced Very High Resolution Radiometers (AVHRR), presented on the native geometry of observation, and spanning 1980 to 2021. \r\n\r\nThe SST CCI AVHRR product contains two different SST estimates. The first is the skin temperature of the water at the time it was observed. The second is an estimate of the temperature at 20 cm depth at either 1030h or 2230h local time, which closely approximates the daily mean SST. Each SST value has an associated total uncertainty estimate, and uncertainty estimates for various contributions to that total. \r\n\r\nThe dataset has been produced as part of the version 3 Climate Data Record (CDR) produced by the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project (ESA SST_cci). The CDR accurately maps the surface temperature of the global oceans over the period 1980 to 2021 using observations from many satellites, with a high degree of independence from in situ measurements. The data provide independently quantified SSTs to a quality suitable for climate research. \r\n\r\nThis CDR Version 3.0 product supersedes the CDR v2.1 product. Compared to the previous version the major changes are: \r\n\r\n* Longer time series: 1980 to 2021 (previous CDR was Sept 1981 to 2016) \r\n\r\n* Improved retrieval to reduce systematic biases using bias-aware optimal methods (for single view sensors) \r\n\r\n* Improved retrieval with respect to desert-dust aerosols \r\n\r\n* Addition of early AVHRR/1 data in 1980s, and improved AVHRR processing to reduce data gaps in 1980s \r\n\r\n* Use of full-resolution MetOp AVHRR data (previously used ‘global area coverage’ Level 1 data) \r\n\r\nData are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ \r\n\r\nWhen citing this dataset please also cite the associated data paper:  \r\n\r\nEmbury, O., Merchant, C.J., Good, S.A., Rayner, N.A., Høyer, J.L., Atkinson, C., Block, T., Alerskans, E., Pearson, K.J., Worsfold, M., McCarroll, N., Donlon, C. Satellite-based time-series of sea-surface temperature since 1980 for climate applications. Scientific Data 11, 326 (2024). https://doi.org/10.1038/s41597-024-03147-w"
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                "title": "Surface level turbulence derived from FAAM flight measurements for the Greenland Flow Distortion EXperiment (GFDex)",
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                "ob_id": 41614,
                "uuid": "85c7d97c5d104a32989b13abc3f8ea5d",
                "short_code": "ob",
                "title": "Elevation change grids of Greenland's periphery for the years 1978, 1981, 1985 and  1987",
                "abstract": "This dataset contains elevation change grids (Digital Elevation Models (DEMs) of Difference) for Greenland's periphery subtracting the AeroDEM (Korsgaard et al., 2016) elevation data from the ArcticDEM (Porter et al., 2023). Four timestamps are available given the four nominal years for AeroDEM acquisition (1978, 1981, 1985, 1987).\r\n\r\nThe .tif files are the first order filtered DoDs (DEM of Difference) of the entire periphery prior to subsequent empiracle rule filtering and clipping to peripheral ice cap and glacier ablation areas.\r\n\r\nKorsgaard, N.J., Nuth, C., Khan, S.A., Kjeldsen, K.K., Bjørk, A.A., Schomacker, A. and Kjær, K.H., 2016. Digital elevation model and orthophotographs of Greenland based on aerial photographs from 1978–1987. Scientific Data, 3(1), pp.1-15.\r\n\r\nPorter, Claire, et al., 2023, “ArcticDEM, Version 4.1”, https://doi.org/10.7910/DVN/3VDC4W, Harvard Dataverse, V1"
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                "short_code": "ob",
                "title": "FAAM C367 CCREST-M flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for CCREST-M FAAM Aircraft Project project."
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            "short_code": "result",
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            "dataPath": "/badc/deposited2024/Seasonal_forecasts_of_JJA_2022/",
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                "ob_id": 41674,
                "uuid": "235e3307338c4168816871a314eada4f",
                "short_code": "ob",
                "title": "ECMWF SEAS5 seasonal forecast output - case study of summer 2022",
                "abstract": "This dataset contains the output of three fully coupled seasonal forecast experiments performed as a case study of summer 2022, using the same model setup as ECWMF seasonal forecast system 5 (SEAS5), as presented in the paper Patterson, M., Befort, D., O'Reilly, C., Weisheimer, A. \"The ECMWF SEAS5 seasonal forecast of the hot and dry European summer of 2022\" Quarterly Journal of the Royal Meteorological Society, https://doi.org/10.1002/qj.4851\r\n​\r\n​The three experiments are:\r\n1) CONTROL\r\nA coupled hindcast ensemble with perturbed initial conditions for the 2022 summer, identical to the operational hindcast with the start date 1st May,  but for 200 members rather than 51.\r\n2) ATMOS-IC-2022\r\nSimilar to the CONTROL with 2022 initial conditions for the atmosphere and land surface and 2022 concentrations of carbon dioxide, but with ocean initial conditions taken from a year in 1981-2021. Five perturbed initial condition states taken for each year hence, 41 x 5 = 205 members.\r\n3) OCEAN-IC-2022\r\nSimilar to CONTROL with 2022 initial conditions for the ocean, but with initial atmosphere, land surface and carbon dioxide conditions taken from a year in 1981-2021. Like ATMOS-IC-2022, 41 x 5 = 205 members.\r\n​\r\nThe OCEAN-IC-2022 experiments were performed by taking the setup for hindcasts 1981-2021 and swapping the ocean initial conditions for 2022. The time dimension therefore corresponds to the time of the base hindcast taken from 1981-2021. Conversely, the ATMOS-IC-2022 were performed by running the 2022 hindcast and swapping the ocean initial conditions for other years. The time dimension for ATMOS-IC-2022 therefore is identical for all members. Hindcasts are all started on 1st May and run for four months.\r\n\r\nThe data stored is monthly-mean output from the experiments with the following directory structure\r\nfor variables on pressure levels:\r\n[experiment_name]/pl/[experiment_name]_[ensemble_member_number]_pl_[level]hPa.nc\r\n\r\nand for those on a single level or at the surface:\r\n[experiment_name]/sfc/[experiment_name]_[ensemble_member_number]_sfc.nc\r\n\r\nThe files each contain multiple variables. Variables on pressure levels are stored at 700hPa,500hPa and 250hPa levels.\r\n\r\nPressure level variables stored are:\r\nu (zonal wind), v (meridional wind), t (air temperature), z (geopotential), q (specific humidity)\r\n\r\nSurface variables stored are:\r\nsst (sea surface temperature), swvl1 (Volumetric soil water layer 1), swvl2 (Volumetric soil water layer 2), swvl3 (Volumetric soil water layer 3), swvl4 (Volumetric soil water layer 4), msl (air pressure at mean sea level), t2m (2m temperature), tprate (mean total precipitation rate)."
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            "short_code": "result",
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                "uuid": "b94cbe2ae4bf45cfa8dc58e98170c07c",
                "short_code": "ob",
                "title": "ESA Land Surface Temperature Climate Change Initiative (LST_cci): Land surface temperature from the Metop-A AVHRR (Advanced Very High Resolution Radiometer) instrument, level 3 collated (L3C) global product, version 1.10",
                "abstract": "This dataset contains daily land surface temperatures (LSTs) and their uncertainty estimates from the Advanced Very High Resolution Radiometer 3 (AVHRR-3) on  the Metop-A satellite. Satellite land surface temperatures are skin temperatures, which means, for example, the temperature of the ground surface in bare soil areas, the temperature of the canopy over forests, and a mix of the soil and leaf temperature over sparse vegetation. The skin temperature is an important variable when considering surface fluxes of, for instance, heat and water.\r\n\r\nDaytime and night-time temperatures are provided in separate files corresponding to the morning and evening METOP-A equator crossing times which are 9.30 and 21:30 local solar time. Per pixel uncertainty estimates are given in two forms, first, an estimate of the total uncertainty for the pixel and second, a breakdown of the uncertainty into components by correlation length. Also provided in the files, on a per pixel basis, are the observation time, the satellite viewing and solar geometry angles, a quality flag, and land cover class.\r\n\r\nThe dataset coverage is global over the land surface. LSTs are provided on a global equal angle grid at a resolution of 0.01° longitude and 0.01° latitude. The daily files have gaps where the surface is not covered by the satellite swath during day or night on that day. Furthermore, LSTs are not produced where clouds are present since under these circumstances the IR radiometer observes the cloud top which is usually much colder than the surface.\r\n\r\nDataset coverage starts on 1st March 2007 and ends on 15th November 2021. There are minor interruptions  during satellite/instrument maintenance periods or instrument anomalies.\r\n\r\nThe dataset was produced by the University of Leicester (UoL) and LSTs were retrieved using the (UoL) LST retrieval algorithm and data were processed in the UoL processing chain.\r\n\r\nThe dataset was produced as part of the ESA Land Surface Temperature Climate Change Initiative which strives to improve satellite datasets to Global Climate Observing System (GCOS) standards."
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                "short_code": "ob",
                "title": "ESA Greenland Ice Sheet Climate Change Initiative (Greenland_Ice_Sheet_cci): Greenland Gravimetric Mass Balance from GRACE data, derived by DTU Space, v2.2",
                "abstract": "This dataset provides a Gravimetric Mass Balance (GMB) product for the Greenland Ice Sheet (GIS), generated by DTU Space, based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through August 2021.\r\n\r\nThe GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended by means of GRACE-FO data starting from June 2018, thus including 200 monthly solutions. The mass change estimation is based on inversion method developed at DTU Space.\r\n\r\nTwo different types of products are available. First, the  gridded mass trends product is comprised of ice mass change trends for cells of equal area with 50 km resolution covering the whole GIS. Second, the mass change time series product provides time series of integrated mass changes for 8 drainage basins and the entire GIS.\r\n\r\nReference:\r\nBarletta, V. R., Sørensen, L. S., and Forsberg, R. (2013) 'Scatter of mass changes estimates at basin scale for Greenland and Antarctica', The Cryosphere, 7, 1411-1432, doi:10.5194/tc-7-1411-2013.\","
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                "title": "FAAM C368 CCREST-M flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project.  This flight took place on 20th February 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project. This flight took place on 21st February 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project. This flight took place on 13th March 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project. This flight took place on 13th March 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project. This flight took place on the 19th March 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project.  This flight took place on the 20th March 2024 over the UK."
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                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project.  This flight took place on the 21st March 2024 over the UK."
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                "title": "FAAM C382 CCREST-M flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft collected for Characterising CiRrus and icE cloud acrosS the spectrTrum (CCREST-M) project.  This flight took place on 25th March 2024 over the UK."
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                "ob_id": 41754,
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                "short_code": "ob",
                "title": "FAAM C383 Instrument Test flight: Airborne atmospheric measurements from core instrument suite on board the BAE-146 aircraft",
                "abstract": "Airborne atmospheric measurements from core instrument suite data on board the FAAM BAE-146 aircraft.  This instrument test flight took place on 17th April 2024 over the UK."
            },
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        {
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                "title": "OpenCLIM: Catchment Discharges",
                "abstract": "This dataset contains simulated river flow discharges from the SHETRAN and HBV hydrological models used in the OpenCLIM (Open CLimate Impacts Modelling framework) project. All values are daily flows (i.e. flow at midnight on the given day) in cumecs. Flows are given for 698 catchments in the UK at catchment outlets/gauge locations: https://nrfa.ceh.ac.uk/data/search. These data can be used for the continued analysis of climate impacts and for comparison with future studies.\r\n\r\nHistorical simulations were driven by CHESS/GEAR (Great Britain) and HADUK-Grid (Northern Ireland) datasets from 01/01/1980 to 31/12/2010 (described below).\r\nClimate change simulations were driven by bias corrected UKCP18 data from from 01/12/1980 to 30/11/2080. \r\n\r\nFile naming convention: Project_Data_Model_ClimateDriver_ScenarioNumber_ScenarioNote_RCMNumber.csv\r\n\r\nScenario notes:\r\nSc01: Land use was taken from baseline Urban Development Model (UDM) setups with no Natural Flood Management (NFM) applied.\r\nSc02 & Sc06: Land use was taken from baseline UDM setups with maximum/moderate NFM were applied (increased woodland and storage). \r\nSc03: Land use was taken from storylined UDM setups (2050 and 2080) for SSPs 2 and 4. No NFM adaptations were applied. Only those (GB) catchments that have UDM changes relative to the baseline simulations or the previous UDM year are simulated.\r\n'RCM' refers to the UKCP18 Regional Climate Model time series that was used in the simulation.\r\n\r\nFurther information, including descriptions of the urban development model (UDM) and natural flood management (NFM) setups, is provided in the supplementary README.txt alongside the data.\r\n\r\nInput datasets:\r\nCHESS - The Climate hydrology and ecology research support system (CHESS) provides high-resolution gridded datasets for environmental research. These datasets include data for driving land surface models and hydrological models, as well as model output.\r\nGEAR - The Centre for Ecology & Hydrology – Gridded Estimates of Areal Rainfall (CEH-GEAR) data set was developed to provide reliable 1 km gridded estimates of daily and monthly rainfall for Great Britain (GB) and Northern Ireland (NI) from 1890 onwards. The data set was primarily required to support hydrological modelling.\r\nHadUK-Grid - HadUK-Grid is a collection of gridded climate variables derived from the network of UK land surface observations by the Met Office. \r\nUKCP18 - UK Climate Projections 2018 (UKCP18) is a climate analysis tool that forms part of the Met Office Hadley Centre Climate Programme."
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/datacentre/processing3/cmip6plus/pre-archive/badc/cmip6plus/data/CMIP6Plus/LESFMIP/MOHC/HadGEM3-GC31-LL/hist-piAer",
            "numberOfFiles": 1,
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        {
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH/mon/HadISDHTable/r1/v4-6-0-2023f",
            "numberOfFiles": 8,
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            "observation": {
                "ob_id": 41768,
                "uuid": "c844fc58615a422aa2e7d2fc8bd8cccf",
                "short_code": "ob",
                "title": "HadISDH.land: gridded global monthly land surface humidity data version 4.6.0.2023f",
                "abstract": "This is the HadISDH.land 4.6.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.land is a near-global gridded monthly mean land surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from weather stations. The observations have been quality controlled and homogenised. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023.  \r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nAs in previous years, the annual scrape of NOAAs Integrated Surface Dataset for HadISD.3.4.0.2023f, which is the basis of HadISDH.land, has pulled through some historical changes to stations. This, and the additional year of data, results in small changes to station selection. The homogeneity adjustments differ slightly due to sensitivity to the addition and loss of stations, historical changes to stations previously included and the additional 12 months of data.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013."
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        {
            "ob_id": 41771,
            "uuid": "a84349c53e4a4c5ebf59bfcc213449ac",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-marine/mon/HadISDHTable/r1/v1-6-0-2023f",
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                "ob_id": 41769,
                "uuid": "2fdc37a517b54376ba19d5c7432457d5",
                "short_code": "ob",
                "title": "HadISDH.marine: gridded global monthly ocean surface humidity data version 1.6.0.2023f",
                "abstract": "This is the HadISDH.marine 1.6.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.marine is a near-global gridded monthly mean marine surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships. The observations have been quality controlled and bias-adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. Users are advised to read the update document in the Docs section for full details on all changes from the previous release.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775."
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ukmo-hadobs/data/insitu/MOHC/HadOBS/HadISDH-extremes/mon/HadISDHTable/r1/v1-1-0-2023f",
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            "observation": {
                "ob_id": 41767,
                "uuid": "0a36ca390a5844578905780ed4c78ded",
                "short_code": "ob",
                "title": "HadISDH.extremes: gridded global monthly land surface wet bulb and dry bulb temperature extremes index data version 1.1.0.2023f",
                "abstract": "This is the HadISDH.extremes 1.1.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.extremes is a near-global gridded monthly land surface extremes index climate monitoring product. It is created from in situ sub-daily observations of wet bulb (converted from dew point temperature) and dry bulb temperature from weather stations. The observations have been quality controlled at the hourly level with strict temporal completeness thresholds applied at daily, monthly, annual, climatological and whole period scales to minimise biases. Gridbox months are assessed for inhomogeneity and scores provided (see Homogeneity Score Document in Docs). The data are provided by the Met Office Hadley Centre and this version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for 27 different heat extremes indices based on the ET-SCI (Expert Team on Sector-Specific Climate Indices) framework. These indices capture a range of moderate to severe extremes. They utilise the daily maximum and minimum values of sub-daily dry bulb and wet bulb temperature observations. Note that these will most likely underestimate the true extremes even when hourly data are available. The data are designed for assessing large scale features over long time scales, ideally using the anomaly fields as these are less affected by sampling biases. Users are advised to cross-compare with national datasets other supporting evidence when assessing small scale localised features.\r\n\r\nThis version is the first with annual updates envisaged. An update record will be maintained in the Docs section.\r\n\r\nHadISD.3.4.0.2023f is the basis of HadISDH.extremes.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K, 2023: HadISDH.extremes Part 1: a gridded wet bulb temperature extremes index product for climate monitoring. Advances in Atmospheric Sciences, 40, 1952–1967, doi: 10.1007/s00376-023-2347-8. https://link.springer.com/article/10.1007/s00376-023-2347-8\r\n\r\nWillett, K. 2023: HadISDH.extremes Part 2: exploring humid heat extremes using wet bulb temperature indices. Advances in Atmospheric Sciences, 40, 1968–1985, doi: 10.1007/s00376-023-2348-7. https://link.springer.com/article/10.1007/s00376-023-2348-7\r\n\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1"
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            "observation": {
                "ob_id": 41766,
                "uuid": "1de7b50d827b4b4f966bd4e3ec5516ea",
                "short_code": "ob",
                "title": "HadISDH.blend: gridded global monthly land and ocean surface humidity data version 1.5.0.2023f",
                "abstract": "This is the HadISDH.blend 1.5.0.2023f version of the Met Office Hadley Centre Integrated Surface Dataset of Humidity (HadISDH). HadISDH.blend is a near-global gridded monthly mean surface humidity climate monitoring product. It is created from in situ observations of air temperature and dew point temperature from ships and weather stations. The observations have been quality controlled and homogenised / bias adjusted. Uncertainty estimates for observation issues and gridbox sampling are provided (see data quality statement section below). These data are provided by the Met Office Hadley Centre. This version spans 1/1/1973 to 31/12/2023.\r\n\r\nThe data are monthly gridded (5 degree by 5 degree) fields. Products are available for temperature and six humidity variables: specific humidity (q), relative humidity (RH), dew point temperature (Td), wet bulb temperature (Tw), vapour pressure (e), dew point depression (DPD).\r\n\r\nThis version extends the previous version to the end of 2023. It combines the latest version of HadISDH.land and HadISDH.marine and therefore their respective update notes. Users are advised to read the update documents in the Docs section for full details.\r\n\r\nTo keep informed about updates, news and announcements follow the HadOBS team on twitter @metofficeHadOBS.\r\n\r\nFor more detailed information e.g bug fixes, routine updates and other exploratory analysis, see the HadISDH blog: http://hadisdh.blogspot.co.uk/\r\n\r\nReferences:\r\n\r\nWhen using the dataset in a paper please cite the following papers (see Docs for link\r\nto the publications) and this dataset (using the \"citable as\" reference):\r\n\r\nWillett, K. M., Dunn, R. J. H., Kennedy, J. J. and Berry, D. I., 2020: Development of\r\nthe HadISDH marine humidity climate monitoring dataset. Earth System Sciences Data,\r\n12, 2853-2880, https://doi.org/10.5194/essd-12-2853-2020\r\n\r\nFreeman, E., Woodruff, S. D., Worley, S. J., Lubker, S. J., Kent, E. C., Angel, W. E.,\r\nBerry, D. I., Brohan, P., Eastman, R., Gates, L., Gloeden, W., Ji, Z., Lawrimore, J.,\r\nRayner, N. A., Rosenhagen, G. and Smith, S. R., ICOADS Release 3.0: A major update to\r\nthe historical marine climate record. International Journal of Climatology.\r\ndoi:10.1002/joc.4775.\r\n\r\nWillett, K. M., Dunn, R. J. H., Thorne, P. W., Bell, S., de Podesta, M., Parker, D. E.,\r\nJones, P. D., and Williams Jr., C. N.: HadISDH land surface multi-variable humidity and\r\ntemperature record for climate monitoring, Clim. Past, 10, 1983-2006,\r\ndoi:10.5194/cp-10-1983-2014, 2014.\r\n\r\nDunn, R. J. H., et al. 2016: Expanding HadISD: quality-controlled, sub-daily station\r\ndata from 1931, Geoscientific Instrumentation, Methods and Data Systems, 5, 473-491.\r\n\r\nSmith, A., N. Lott, and R. Vose, 2011: The Integrated Surface Database: Recent\r\nDevelopments and Partnerships. Bulletin of the American Meteorological Society, 92,\r\n704-708, doi:10.1175/2011BAMS3015.1\r\n\r\nWe strongly recommend that you read these papers before making use of the data, more\r\ndetail on the dataset can be found in an earlier publication:\r\n\r\nWillett, K. M., Williams Jr., C. N., Dunn, R. J. H., Thorne, P. W., Bell, S., de\r\nPodesta, M., Jones, P. D., and Parker D. E., 2013: HadISDH: An updated land surface\r\nspecific humidity product for climate monitoring. Climate of the Past, 9, 657-677,\r\ndoi:10.5194/cp-9-657-2013."
            },
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            "uuid": "fe08442d20a14ca79d25f59aa32755fe",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c335-dec-20",
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            "volume": 0,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/faam/data/2022/c334-dec-19",
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            "volume": 0,
            "fileFormat": "Data are netCDF and NASA-Ames formatted. Ancillary files may be plain ASCII or PDF formatted. Image files may be PNG formatted.",
            "storageStatus": "online",
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        },
        {
            "ob_id": 41779,
            "uuid": "5e15d6545a6e491bad96d929478467f5",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/neodc/esacci/biomass/data/agb/maps/v5.0/",
            "numberOfFiles": 8798,
            "volume": 664510290791,
            "fileFormat": "Data are netCDF and geotiff formatted.",
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            "observation": {
                "ob_id": 41620,
                "uuid": "02e1b18071ad45a19b4d3e8adafa2817",
                "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",
                "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."
            },
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        {
            "ob_id": 41780,
            "uuid": "6c0952eb5f5a45e2b6d0df0b5f38685c",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/accacia/data/turbulence_SL",
            "numberOfFiles": 3,
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            "observation": {
                "ob_id": 41659,
                "uuid": "83ba8d0333ba4ccd99c9d3df3096df24",
                "short_code": "ob",
                "title": "Surface level turbulence derived from FAAM and MASIN flight measurements for the Aerosol Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) project",
                "abstract": "This dataset contains surface layer turbulent fluxes and meteorological variables derived from instruments on board the Facility for Airborne Atmospheric Measurement (FAAM) and Meteorological Airborne Science Instrumentation (MASIN) instrumentation measurements onboard the British Antarctic Survey (BAS) Twin Otter aircraft. These data were based on measurements made during the Aerosol Cloud Coupling and Climate Interactions in the Arctic (ACCACIA) project."
            },
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        {
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            "uuid": "fb1a42ff430349139227551e3b9ef7d1",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/cmip6/data/CMIP6Plus/DeepMIP/deepmip-eocene-p1",
            "numberOfFiles": 3625,
            "volume": 180356199122,
            "fileFormat": "Data are Net-CDF formatted",
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            "observation": {
                "ob_id": 41535,
                "uuid": "95aa41439d564756950f89921b6ef215",
                "short_code": "ob",
                "title": "Deep-Time Model Intercomparison Project (DeepMIP) Eocene model data version 1.0",
                "abstract": "This dataset contains output from 35 climate model simulations of the warm early Eocene Climatic Optimum (EECO; ∼ 50 million years ago) and corresponding preindustrial reference experiments. The dataset combines global model output from nine coupled climate models (CESM1.2-CAM5, COSMOS-landveg-r2413, GFDL-CM2.1, HadCM3B-M2.1aN, HadCM3BL-M2.1aN, INM-CM4-8, IPSLCM5A2, MIROC4m and NorESM1-F) that have carried out coordinated simulations as part of the Deep-Time Model Intercomparison Project (DeepMIP). Climatological means and time series data of the respective last 100 model years for a total of 57 atmospheric and oceanic variables are standardised following the CMIP6 data request and CF metadata convention."
            },
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        {
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            "uuid": "360f24983ac34bddbd37dbdf5420ebef",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/Greenland_Landcover_Grids/",
            "numberOfFiles": 3,
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            "observation": {
                "ob_id": 41662,
                "uuid": "667a2328d2b04f02b653569975f53f55",
                "short_code": "ob",
                "title": "Greenland 1980 and 2010s landcover grids from Landsat 5 and Landsat 8",
                "abstract": "This dataset consists of two landcover grids representing Greenland in the late 1980s and late 2010s, utilising Landsat 5 Thematic Mapper Top-Of-Atmosphere (TM TOA) and Landsat 8 Operational Land Imager Top-Of-Atmosphere (OLI TOA) imagery respectively. \r\nThe data creation involved rigorous preprocessing and image classification methodologies, detailed extensively in the paper by Grimes, M., Carrivick, J.L., Smith, M.W., et al. (2024), \"Land cover changes across Greenland dominated by a doubling of vegetation in three decades,\" Sci Rep, 14, 3120. DOI: 10.1038/s41598-024-52124-1. The full methodology is also discussed in the supplementary material of the publication.\r\n\r\nThe resultant .tif grids are in integer format with values from 1 to 9 representing landcover class:\r\n1 - Bad data/Cloud/Shadow\r\n2 - Snow and Ice\r\n3 - Wet ice and meltwater\r\n4 - Freshwater\r\n5 - Coarse sediment\r\n6 - Fine-grained sediment\r\n7 - Bedrock\r\n8 - Tundra vegetation\r\n9 - Dense/wet vegetation\r\nThe tif grids were produced using Google Earth Engine. All summer Landsat imagery was filtered by metadata, followed by topographical correction, resulting in a best-pixel mosaic for Greenland's periphery. Band ratios (NDSI, NDVI, NDWI) were computed and stacked with visible, NIR, and SWIR bands. A principal component analysis was conducted, retaining the first six principal components as bands, which were subsequently classified using a K-means clusterer and refined with a supervised random-forest classifier and a slope threshold was applied to discriminate shadows from dark water bodies more effective.\r\nThis dataset was generated through a NERC-funded PhD project at the University of Leeds (Grant NE/L002574/1)."
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        {
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            "uuid": "c472d84c38574b44b2ef1f94393a6461",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/deposited2024/Andes_GICs_LIA",
            "numberOfFiles": 9,
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            "observation": {
                "ob_id": 41533,
                "uuid": "7545a606606c4e9bb6139dfc21a95264",
                "short_code": "ob",
                "title": "Andes glaciers and ice caps outlines during the Little Ice Age (LIA)",
                "abstract": "This dataset comprises a shapefile of >5500 outlines of glaciers and ice caps during the Little Ice Age (LIA) approximately 1400 to 1850, spanning across the Andes from the equator to -60 degrees South. These outlines have been manually digitised by reshaping Randolph Glacier Inventory version 6 (RGI_v6) (https://doi.org/10.7265/4m1f-gd79) outlines to the extent interpreted to best represent the LIA. The LIA extent was deduced manually-visually by interpreting geomorphological evidence; primarily moraine crests and trimlines, within high-resolution aerial imagery; primarily WorldView and GeoEye. More methodological details and usage of this dataset for analysis of glacier area changes from the LIA to post year 2000 are presented in the parent paper: Carrivick et al., 2024. Accelerating glacier area loss across the Andes since the Little Ice Age. Geophysical Research Letters (submitted)."
            },
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        },
        {
            "ob_id": 42341,
            "uuid": "5e836cce035045599d761f187fbb972b",
            "short_code": "result",
            "curationCategory": "",
            "dataPath": "/badc/deposited2024/OpenCLIM_CatchmentFlowMetrics/",
            "numberOfFiles": 338,
            "volume": 165172087,
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            "observation": {
                "ob_id": 41670,
                "uuid": "81567bfb789e4ec4ae30cdd3772f8242",
                "short_code": "ob",
                "title": "OpenCLIM - Catchment Flow Metrics",
                "abstract": "This dataset contains flood, drought, and flow metrics calculated as part of the OpenCLIM (Open CLimate Impacts Modelling framework) project. All metrics have been calculated for 698 UK catchments from timeseries of daily river flow simulated by the hydrological models SHETRAN and HBV by teams from Newcastle University and the University of East Anglia respectively for historical and future periods. All analysis code is available on GitHub (https://github.com/OpenCLIM/OpenCLIM-SHETRAN_Flow_Analysis).\r\n\r\nClimate change simulations were driven by bias corrected UKCP18 data from 01/12/1980 to 30/11/2080. Datasets from all 12 regional climate models (RCMs) were used and are presented separately. Flow metrics are given for 698 catchments in the UK at catchment outlets/gauge locations: https://nrfa.ceh.ac.uk/data/search. These data can be used for the continued analysis of climate impacts and for comparison with future studies.\r\n\r\nFile naming convention: Project_Data_Model_ClimateDriver_ScenarioNumber_ScenarioNote_Metric.csv\r\n\r\nScenario notes:\r\nSc01:  Land use was taken from baseline Urban Development Model (UDM) setups with no Natural Flood Management (NFM) adaptations applied. \r\nSc02 & Sc06: Land use was taken from baseline UDM setups with NFM max/moderate adaptations applied. \r\nSc03: Land use was taken from storylined UDM setups (2050 and 2080) for SSPs 2 and 4. No NFM adaptations were applied.  For SHETRAN, only catchments with UDM changes relative to the baseline simulations or the previous UDM year were simulated.\r\n\r\nFurther information, including descriptions of the urban development model (UDM) and natural flood management (NFM) setups, is provided in the supplementary README.txt alongside the data.\r\n\r\nUKCP18 - UK Climate Projections 2018 (UKCP18) is a climate analysis tool that forms part of the Met Office Hadley Centre Climate Programme."
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        {
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            "short_code": "result",
            "curationCategory": "A",
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            "observation": {
                "ob_id": 39468,
                "uuid": "93f2cf31702d4cf3a35f35f899fc0c6b",
                "short_code": "ob",
                "title": "MOSAiC: Wind profiles and acoustic backscatter from a Scintec MFAS Sodar on Icebreaker Polarstern -version 1.0",
                "abstract": "Wind profiles and acoustic backscatter from a Scintec Flat Array Sodar (MFAS) Sodar deployed on the sea ice during for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate\r\n(MOSAiC). Variables include vertical backscatter, intensity, mean wind speed and direction, wind components, and standard deviations of the wind variables - in the instrument reference frame - along with wind speed and direction in the earth frame, and quality control variables at 10 minute intervals. Also vertical backscatter only at 5 minute intervals.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure."
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            "uuid": "66b13b39d1164d3da03eb4011b123b7d",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ncas-mobile/data/ncas-lidar-wind-profiler-1/20191005_mosaic/v3.0/",
            "numberOfFiles": 593,
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            "observation": {
                "ob_id": 40841,
                "uuid": "86d4b9195b40469e920cb56044adb265",
                "short_code": "ob",
                "title": "MOSAiC: Wind profiles from Galion G4000 Lidar Wind Profiler - Version 3",
                "abstract": "Wind profiles from a Galion G4000 Doppler lidar for the international Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) project, derived from conical scans at 30 degree and 50 degree beam elevation angles.\r\n\r\nThe University of Leeds participation in the project- MOSAiC Boundary Layer -was funded by the Natural Environment Research Council (NERC, grant: NE/S002472/1) and involved instrumentation from the Atmospheric Measurement and Observations Facility of the UK's National Centre for Atmospheric Science (NCAS AMOF). This was a year-long project on the German icebreaker Polarstern to study Arctic climate focused on measurements of atmospheric boundary layer dynamics and turbulent structure. The Galion wind profiler provides high resolution (~15m vertical and 5 minute temporal) measurements of wind profiles. Data are only available where sufficient particles are available to backscatter the laser light - in the clean arctic environment, this requires cloud or precipitation.\r\n\r\nThis is version 3 of this dataset which corrects an error in the implementation of the correction\r\nof the lidar azimuth when the scanning head slipped at very low temperatures."
            },
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        {
            "ob_id": 42760,
            "uuid": "f12ca8e5ee8d43a2a4406343398b2822",
            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ncas-longterm-obs/data/ncas-mobile-xband-radar/20161101-20180604",
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            "observation": {
                "ob_id": 31948,
                "uuid": "ffc9ed384aea471dab35901cf62f70be",
                "short_code": "ob",
                "title": "NCAS mobile X-band radar scan data from 1st November 2016 to 4th June 2018 deployed on long-term observations at the Chilbolton Facility for Atmospheric and Radio Research (CFARR), Hampshire, UK",
                "abstract": "This dataset contains scan data from the National Centre for Atmospheric Science Atmospheric Measuring Facility's mobile X-band radar between 1st November 2016 to 4th June 2018 at Chilbolton Facility for Atmospheric and Radio Research (CFARR), UK,  as part of ongoing long-term observations made by the NERC National Centre for Atmospheric Science (NCAS).   The radar transmits pulses of electromagnetic radiation and measures the amount of energy backscattered to the receiver from which the location and intensity of precipitation, radial winds and polarisation parameters can be calculated.  \r\n\r\nParameters available in these data files include:\r\ndBZ - equivalent reflectivity factor;\r\nV - radial velocity;\r\nW - spectral width;\r\nZDR - differential reflectivity;\r\nKDP - specific differential phase shift;\r\nPhiDP - differential phase shift;\r\nRhoHV - co-polar cross correlation coefficient;\r\nSQI - signal quality index or normalized_coherent_power.\r\nA complete list of all available parameters is available on the CEDA data catalogue record for this dataset.\r\n\r\nThe sur files contain a volume of scans at different elevation angles between 0 and 90 degrees, approximately every 5-6 minutes. \r\nThe rhi files contain a single cross-section scan at a given azimuth and an elevation range of 0 to 180 degrees, every 5-6 minutes.\r\n\r\nThe data are available as netCDF files to all registered CEDA users under the Open Government License."
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                "ob_id": 5415,
                "uuid": "1a4c08083b930e167ad9fba6d2087ee4",
                "short_code": "ob",
                "title": "Vertical wind profile data from 15th April to 19th April 2004 measured by the University of Manchester 1290 mhz mobile wind profiler deployed on long term observations at Met Office Research Unit, Cardington, Bedfordshire",
                "abstract": "The UK's Natural Environment Research Council's (NERC) National Centre for Atmospheric Sciences (NCAS) operates a suite of instrumentation to monitor the atmospheric dynamics and composition of the atmosphere. This dataset brings together all the long term routine observations made by NCAS instruments covering surface based instruments as well as remote sensing instruments such as radars and lidars. Some of the instruments may also be deployed elsehwere on field campaigns, for which the data will be available under the associated field campaign dataset. Links are also available to pages describing the instruments from which links to all data from that particular instrument can be found.\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"
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            "volume": 0,
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            "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.",
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            "ob_id": 42764,
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ncas-longterm-obs/data/ncas-mobile-xband-radar/20161101-20180604",
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            "volume": 0,
            "fileFormat": "Data are netCDF formatted.",
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            "short_code": "result",
            "curationCategory": "A",
            "dataPath": "/badc/ncas-longterm-obs/data/man-radar-1290mhz/20040415-20040419",
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