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  • Solar-induced Chlorophyll Fluorescence (SIF) data created from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using an adapted version of the University of Leicester Full-Physics retrieval scheme (UoL-FP). These dataset contains both Level 2 and Level 3 S-polarised SIF. The Level 2 data are in daily files and are not averaged, whilst the Level 3 data are averaged spatially and temporally on both a monthly and weekly timescale. The SIF data was derived from L1B data from the TANSO-FTS ( Thermal and Near Infrared Sensor for carbon Observation - Fourier Transform Spectrometer) instrument on the GOSAT satellite. For each GOSAT sounding, the S-polarised spectra have been extracted from two narrow micro-windows outside the Oxygen A-band, at around 755 nm and 772 nm. For more information on the retrieval setup and bias correction, please see "Novel Methods for Atmospheric Carbon Dioxide Retrieval from the JAXA GOSAT and NASA OCO-2 Satellites - Part II: Remote Sensing of Chlorophyll Fluorescence" by Peter Somkuti.

  • The University of Leicester GOSAT Proxy XCH4 v9.0 data set contains column-averaged dry-air mole fraction of methane (XCH4) generated from the Greenhouse Gas Observing Satellite (GOSAT) Level 1B data using the University of Leicester Full-Physics retrieval scheme (UoL-FP) using the Proxy retrieval approach. This data is an NCEO funded update/extension to the European Space Agency Climate Change Initiative (CCI) CH4_GOS_OCPR V7.0. and the Copernicus Climate Change Service (C3S) CH_4 v7.2 data sets. It's a full reprocessing, based on different underlying L1B radiance data with additional changes. The latest version of the GOSAT Level 1B files (version 210.210) was acquired directly from the National Institute for Environmental Studies (NIES) GOSAT Data Archive Service (GDAS) Data Server and are processed with the Leicester Retrieval Preparation Toolset to extract the measured radiances along with all required sounding-specific ancillary information such as the measurement time, location and geometry. These measured radiances have the recommended radiometric calibration and degradation corrections applied as per Yoshida et al., 2013 with an estimate of the spectral noise derived from the standard deviation of the out-of-band signal. The spectral data were then inputted into the UoL-FP retrieval algorithm where the Proxy retrieval approach is used to obtain the column-averaged dry-air mole fraction of methane (XCH4). Post-filtering and bias correction against the Total Carbon Column Observing Network is then performed. See process information and documentation for further details.

  • The CH4_GOS_OCFP 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 University of Leicester Full-Physics Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version is version 2.1 and forms part of the Climate Research Data Package 4. The University of Leicester Full-Physics Retrieval Algorithm is based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and has been modified for use on GOSAT spectra. A second GOSAT CH4 product, generated using the SRFP algorithm, is also available. The XCH4 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG).

  • Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project, the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). The University of Leicester Full-Physics Retrieval Algorithm has been applied to the TANSO-FTS data, based on the original Orbiting Carbon Observatory (OCO) Full Physics Retrieval Algorithm and modified for use on GOSAT spectra. A second product, generated using the SRFP algorithm, is also available. The XCO2 product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further information, including details of the OCFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) in the documentation section. The GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/

  • Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and Climate Research Data Package Number 2 (CRDP#3), the XCO2 GOSAT product comprises a level 2, column-averaged dry-air mole fraction (mixing ratio) for carbon dioxide (CO2). The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). In this case, the RemoTeC Full Physics (SRFP) algorithm, jointly developed at SRON and KIT, has been applied to the TANSO-FTS data. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available. 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 standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document in the documentation section. The GHG-CCI team encourage all users of their products to register with them to receive information on any updates or issues regarding the data products and to receive notification of new product releases. To register, please use the following link: http://www.iup.uni-bremen.de/sciamachy/NIR_NADIR_WFM_DOAS/CRDP_REG/

  • 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.

  • 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_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 CH4_GOS_OCPR dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (XCH4.) The product has been produced using data acquired from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT), using the OCPR University of Leicester Proxy Retrieval Algorithm. It has been generated as part of the European Space Agency (ESA) Greenhouse Gases Climate Change Initiative (GHG_cci). This version of the data is v7.0 and forms part of the Climate Research Data Package 4. This algorithm has been designated the baseline algorithm for the GHG CCI proxy methane retrievals. A second product has also been generated from the TANSO-FTS data using an alternative algorithm, the RemoTeC Proxy algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the OCPR baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The product is stored in NetCDF format with all GOSAT soundings on a single day stored in one file. For further details on the product, including the UoL-PR algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents.

  • The CO2_GOS_SRFP dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) for carbon dioxide (XCO2), from the Thermal and Near Infrared Sensor for Carbon Observations (TANSO-FTS) NIR and SWIR spectra, onboard the Japanese Greenhouse gases Observing Satellite (GOSAT). It has been produced using the RemoTeC Full Physics (SRFP) algorithm, v2.3.8, by the Greenhouse Gases Climate Change Initiative (GHG_cci) project. This forms part of the GHG_cci Climate Research Data Package Number 4 (CRDP#4). The RemoTeC Full Physics (SRFP) algorithm has been jointly developed at SRON and KIT. A second product, generated using the OCFP (University of Leicester Full Physics) algorithm, is also available, and is considered the GHG_cci baseline product, whilst the SRFP product forms an 'alternative' product. It is advised that users who aren't sure whether to use the baseline or alternative product use the OCFP product. For more information on the differences between baseline and alternative algorithms please see the Greenhouse Gases 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. The product file contains the key standard products, i.e. the retrieved column averaged dry air mixing ratio XCO2 with bias correction, averaging kernels and quality flags, as well as secondary products specific for the RemoTeC algorithm. For further information, including details of the SRFP algorithm and the TANSO-FTS instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.