{"count":4009,"next":null,"previous":"https://catalogue.ceda.ac.uk/api/v3/computations/?format=json&limit=100&offset=3900","results":[{"ob_id":46335,"uuid":"c58661a8eda64ed69188a1f51352f77e","short_code":"comp","title":"Derivation of the ESA SATACI Climate Change Initiative (SATACI_cci): CISAR combined hourly AOD/COD products from MSG / SEVIRI, version 1.0.0","abstract":"The data have been derived using the Combined Inversion of Surface and Aerosols (CISAR) algorithm applied to observations from the SEVIRI (Spinning Enhanced Visible and Infrared Imager) onboard the MSG1 and MSG4 satellites.\r\n\r\nFor further information please see the project documentation","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46351,"uuid":"0ed19ce77d4146b08aa950fb8877f8f1","short_code":"comp","title":"Derivation of Gravimetric Mass Balance products from the Antarctic Ice Sheet CCI (v5.2)","abstract":"TU Dresden has generated a Gravimetric Mass Balance (GMB) product for the Antarctic Ice Sheet (AIS) based on monthly snapshots of the Earth’s gravity field provided by the Gravity Recovery and Climate Experiment (GRACE) and its follow-on satellite mission (GRACE-FO). The product relies on monthly gravity field solutions (L2) of release 06 generated at the Center for Space Research (University of Texas at Austin) and spans the period from April 2002 through July 2020. The GMB product covers the full GRACE mission period (April 2002 - June 2017) and is extended to December 2025 by means of GRACE-FO data starting from June 2018, thus including 240 monthly solutions. The mass change estimation is based on the tailored sensitivity kernel approach developed at TU Dresden.\r\n\r\nThe methodology is described in: Groh, A. & Horwath, M. (2021). Antarctic Ice Mass Change Products from GRACE/GRACE-FO Using Tailored Sensitivity Kernels. Remote Sens., 13(9), 1736. doi:10.3390/rs13091736","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46353,"uuid":"ef1232ad1cd14840ac787ef7a51bbcd8","short_code":"comp","title":"Derivation of Antarctic Ice Sheet Surface Elevation Change (SEC) for the ESA Antarctic CCI project","abstract":"Surface elevation change products are derived from radar altimetry data acquired by ERS-1 (1991–1996), ERS-2 (1995–2003), ENVISAT (2002–2012), CryoSat-2 (2010–present), Sentinel-3A (2016–present), and Sentinel-3B (2018–present), and from laser altimetry data acquired by ICESat-2 (2018–present). Cross-calibration of radar altimetry missions was performed to produce consistent multi-mission records. Processing was carried out by CPOM. Products are provided on a 5 km south polar stereographic grid (EPSG:3031). Single-mission SEC products represent the full operational period of each mission. Multi-mission products combine all available radar altimetry missions.","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46370,"uuid":"ab0423013cf6494082b964d88e0841e3","short_code":"comp","title":"Harmonisation of data with GPS using Python","abstract":"Harmonisation of data with GPS using python scripts developed specifically for the MOMENTUM project.","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46377,"uuid":"23c1ca43ed014506871a3ef257ad2181","short_code":"comp","title":"Derivation of the ESA Lakes Climate Change Initiative (Lakes_cci): Harmonised lake surface water temperature and lake Ice cover gap-filled product, Version 1.0","abstract":"The dataset was derived from the Level3 Lake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) product from the ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0 dataset (https://doi.org/10.5285/a56dba09df8a42ec9fba8b8c7a5e1f69)\r\n \r\nLake Surface Water Temperature (LSWT) and Lake Ice Cover (LIC) were gap-filled independently using separate algorithms and subsequently harmonised to produce a consistent LSWT+LIC dataset.\r\n\r\nDetailed descriptions of the gap-filling methods, algorithm selection process, and harmonisation approach are provided in three journal articles. Further information on the dataset is available in these publications and in the Product User Guide of the ESA Lakes Climate Change Initiative (Lakes_cci): Lake products, Version 3.0.","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46378,"uuid":"2537abe9508349b2b21397a46c2b449a","short_code":"comp","title":"Derivation of the ESA SATACI Climate Change Initiative (SATACI_cci): ORAC/BUGSRad cloud, aerosol and flux products from MSG / SEVIRI, version 3.0.0","abstract":"This dataset has been derived using the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm applied to observations from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the MSG4 satellite, as well as fluxes derived using the BUGSRad algorithm.\r\n\r\nFor further information please see the project documentation","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46383,"uuid":"88bb8f86e608464bbda231a507a98056","short_code":"comp","title":"STOCHEM-CRI","abstract":"","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46385,"uuid":"d3a1711a7c5f410ea9a7e20180ff3330","short_code":"comp","title":"Derivation of the ESA Climate Change Initiative River Discharge Water Level product, v2.1","abstract":"For information on the derivation of the Water Level dataset see the project documentation \r\n(https://climate.esa.int/projects/river-discharge)","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]},{"ob_id":46391,"uuid":"e7fe7bd4a7ca48debe132a2eca2c8b91","short_code":"comp","title":"Sentinel-2 dry-season compositing and Random Forest land-cover classification in Google Earth Engine, with chainage-level summary statistics generated in ArcGIS Pro using the Tabulate Area tool.","abstract":"Land-cover mapping was performed in Google Earth Engine using ee.Classifier.smileRandomForest (500 trees) was trained on manually digitised polygons sampling four classes (vegetated, non-vegetated, water, sand) from Sentinel-2 L2A Surface Reflectance Harmonized median composites of June-August each target year. Cloud and shadow masking used the Scene Classification Layer with a 1-2 pixel buffer. Classification used bands B2, B3, B4, B8 and B11 with a 70/30 train-test split, validated using overall accuracy, Cohen's Kappa, Producer's Accuracy and User's Accuracy (Congalton, 1991). A majority filter was applied to reduce salt-and-pepper noise. Chainage-level metrics were generated in ArcGIS Pro using the Tabulate Area tool, with 10 km chainage polygons as zone features and the classified rasters / canopy-height raster as class rasters. The CHIRPS June-August 2024 cumulative rainfall raster was aggregated using Zonal Statistics (mean) over the same chainage polygons.","keywords":"","inputDescription":null,"outputDescription":null,"softwareReference":null,"imageDetails":[],"identifier_set":[]}]}