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  • The CH4_GOS_SRFP dataset is comprised of level 2, column-averaged mole fractiona (mixing ratioa) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra onboard the Japanese Greenhouse gases Observing Satellite (GOSAT) using the SRFP (RemoTec) algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the dataset is v2.3.8 and forms part of the Climate Research Data Package 4. The RemoTeC SRFP baseline algorithm is a Full Physics algorithm. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. The product file contains the key products with and without bias correction. Information relevant for the use of the data is also included in the data file, such as the vertical layering and averaging kernels. Additionally, the parameters retrieved simultaneously with XCH4 are included (e.g. surface albedo), as well as retrieval diagnostics like retrieval errors and the quality of the fit. For further information on the product, including the RemoTeC Full Physics algorithm and the TANSO-FTS instrument please see the Product User Guide (PUG) or the Algorithm Theoretical Basis Document.

  • This dataset contains permafrost active layer thickness data produced as part of the European Space Agency's (ESA) Climate Change Initiative (CCI) Permafrost project. It forms part of the first version of their Climate Research Data Package (CRDP v1). It is derived from a thermal model driven and constrained by satellite data. Grid products of CDRP v1 are released in annual files, covering the start to the end of the Julian year. The maximum depth of seasonal thaw is provided, which corresponds to the active layer thickness. Case A: It covers the Northern Hemisphere (north of 30°) for the period 2003-2017 based on MODIS Land Surface temperature merged with downscaled ERA5 reanalysis near-surface air temperature data. Case B: It covers the Northern Hemisphere (north of 30°) for the period 1997-2002 based on downscaled ERA5 reanalysis near-surface air temperature data which are bias-corrected with the Case A product for the overlap period 2003-2018 using a pixel-specific statistics for each day of the year.

  • This v2.0 SST_cci Advanced Very High Resolution Radiometer (AVHRR) level 3 uncollated data (L3U) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 - 12/2016. This Level 3 Uncollated (L3U) product provides these SST data on a 0.05 regular latitude-longitude grid with a single orbit per file. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .

  • This v2.0 SST_cci Along-Track Scanning Radiometer (ATSR) Level 3 Collated (L3C) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the ATSR series of satellite instruments. It covers the period 11/1991 - 04/2012. This L3C product provides these SST data on a 0.05 regular latitude-longitude grid and collated to include all orbits for a day (separated into daytime and nighttime files). The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/ .

  • These ancillary datasets were used in the production of the "Active", "Passive" and "Combined" soil moisture data products, created as part of the European Space Agency's (ESA) Soil Moisture Climate Change Initiative (CCI) project. The set of ancillary datasets include datasets of Average Vegetation Optical Depth data from AMSR-E, Soil Porosity, Topographic Complexity and Wetland fraction, as well as a Land Mask. This version of the ancillary datasets were used in the production of the v03.3 Soil Moisture CCI data. The "Active" "Passive" and "Combined" soil moisture products which they were used in the development of are fusions of scatterometer and radiometer soil moisture products, derived from the AMI-WS, ASCAT, SMMR, SSM/I, TMI, AMSR-E, WindSat, AMSR2 and SMOS satellite instruments. To access these products or for further details on them please see their dataset records. Additional reference documents and information relating to them can also be found on the CCI Soil Moisture project website. Soil moisture CCI data should be cited using the complete three references as follows: 1. Dorigo, W.A., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., Chung, D., Ertl, M., Forkel, M., Gruber, A., Haas, E., Hamer, D. P. Hirschi, M., Ikonen, J., De Jeu, R. Kidd, R. Lahoz, W., Liu, Y.Y., Miralles, D., Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. In Remote Sensing of Environment, 2017, ISSN 0034-4257, https://doi.org/10.1016/j.rse.2017.07.001 2. Gruber, A., Dorigo, W. A., Crow, W., Wagner W. (2017). Triple Collocation-Based Merging of Satellite Soil Moisture Retrievals. IEEE Transactions on Geoscience and Remote Sensing. PP. 1-13. 10.1109/TGRS.2017.2734070 3. Liu, Y.Y., Dorigo, W.A., Parinussa, R.M., de Jeu, R.A.M. , Wagner, W., McCabe, M.F., Evans, J.P., van Dijk, A.I.J.M. (2012). Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sensing of Environment, 123, 280-297, doi: 10.1016/j.rse.2012.03.014

  • This v2.0 SST_cci Advanced Very High Resolution Radiometer (AVHRR) Level 2 Preprocessed (L2P) Climate Data Record (CDR) consists of stable, low-bias sea surface temperature (SST) data from the AVHRR series of satellite instruments. It covers the period between 08/1981 and 12/2016. This L2P product provides these SST data on the original satellite swath with a single orbit of data per file. The dataset has been produced as part of the European Space Agency (ESA) Climate Change Initiative Sea Surface Temperature project(ESA SST_cci). The data products from SST CCI accurately map the surface temperature of the global oceans over the period 1981 to 2016 using observations from many satellites. The data provide independently quantified SSTs to a quality suitable for climate research. Data are made freely and openly available under a Creative Commons License by Attribution (CC By 4.0) https://creativecommons.org/licenses/by/4.0/

  • The ESA Sea State Climate Change Initiative (CCI) project has produced global merged multi-sensor time-series of monthly gridded satellite altimeter significant wave height (referred to as Level 4 (L4) data) with a particular focus for use in climate studies. This dataset contains the Version 1.1 Remote Sensing Sea Surface Height product, gridded over a global regular cylindrical projection (1°x1° resolution), averaging valid and good measurements from all available altimeters on a monthly basis (using the L2P products also available). These L4 products are meant for statistics and visualization. This first version of the Sea State CCI products is inherited from the GlobWave project, building on experience and existing outputs. It extends and improves the GlobWave products ,which were a post-processing over existing L2 altimeter agency products with additional filtering, corrections and variables. A major improvement consists in a new denoised sea surface height variable using Empirical Mode Decomposition, which was used as input to these monthly statistical fields. The altimeter data used in the Sea State CCI dataset v1.1 come from multiple satellite missions spanning from 1991 to 2018 (ERS-1, ERS-2, Topex, Envisat, GFO, CryoSat-2, Jason-1, Jason-2, Jason-3, SARAL). Many altimeters are bi-frequency (Ku-C or Ku-S) and only measurements in Ku band were used, for consistency reasons, being available on each altimeter but SARAL (Ka band).

  • The CH4_EMMA dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) for methane (XCH4). It has been produced using the ensemble median algorithm EMMA to several different versions of the Japanes Greenhouse gases Observing Satellite (GOSAT) XCH4 data, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v1.2, and forms part of the Climate Research Data Package 4. The ensemble median algorithm EMMA has been applied to level 2 data of several different retrieval products from the Japanese Greenhouse gases Observing Satellite (GOSAT) This is therefore a merged GOSAT XCH4 Level 2 product, which is primarily used as a comparison tool to assess the level of agreement / disagreement of the various input products (for model-independent global comparison, i.e. for comparisons not restricted to TCCON validation sites and independent of global model data). For further information on the product and the EMMA algorithm please see the EMMA website, the GHG-CCI Data Products webpage or the Product Validation and Intercomparison Report (PVIR).

  • The CH4_GOS_SRPR dataset is comprised of Level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4). It has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the RemoTeC SRPR Proxy Retrieval algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the data is version 2.3.8, and forms part of the Climate Research Data Package 4. This Proxy Retrieval product has been generated using the RemoTeC SRPR algorithm, which is being jointly developed at SRON and KIT. This has been designated as an 'alternative' GHG CCI algorithm, and a separate product has also been generated by applying the baseline GHG CCI proxy algorithm (the University of Leicester OCPR algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the OCPR product generated with the baseline algorithm. For more information regarding the differences between the baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in a single NetCDF file. Retrieval results are provided for the individual GOSAT spatial footprints, no averaging having been applied. As well as containing the key product, the product file contains information relevant for the use of the data, such as the vertical layering and averaging kernels. The parameters which are retrieved simultaneously with XCH4 are also included (e.g. surface albedo), in addition to retrieval diagnostics like quality of the fit and retrieval errors. For further details on the product, including the RemoTeC algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.

  • This dataset consists of gloabl surface air temperature estimates derived from satellite surface skin temperature measurements, with uncertainties provided. It has been compiled as part of the European Union Horizon 2020 EUSTACE (EU Surface Temperature for All Corners of Earth) project. The global surface air temperatures are provided separately for areas over land, sea and ice. These surface air temperatures have been derived from satellite skin temperatures using relationships between air and skin temperatures for each surface derived as part of the EUSTACE project. The air temperatures and uncertainty information are presented in a consistent format.