The Greenhouse Gases Climate Change Initiative (GHG_cci) data products are near-surface-sensitive dry-air column-averaged mole fractions (mixing ratios) of methane (CH4) and carbon dioxide (CO2), created as part of the European Space Agency's (ESA) Greenhouses Gases Essential Climate Variable (ECV) CCI project. Denoted XCO2 (in ppmv) and XCH4 (in ppbv), the products have been retrieved from the SCIAMACHY instrument on ENVISAT and TANSO-FTS onboard GOSAT, using ECV Core Algorithms (ECAs). Other satellite instruments such as IASI, MIPAS and ACE-FTS have also been used to provide constraints for upper layers, with their corresponding retrieval algorithms referred to as Additional Constraints Algorithms (ACAs). The GHG data products are typically updated annually, the corresponding datasets being called Climate Research Data Packages (CRDP). The products have each been generated from individual sensors, a single merged product not having yet been created "combining" the products from different sensors to cover the entire available satellite time series. One merged product has however been generated using the EMMA algorithm, covering a limited time period. This EMMA product is mainly used as a comparison tool for products generated using individual algorithms, making up the collection of products used by EMMA. Typically the same product (e.g. XCO2 from GOSAT) has been generated using different retrieval algorithms. A baseline algorithm has been used to generate one recommended baseline product, for users unsure which product to choose. Other products are called alternative products. However an alternative product's quality may equal that of the corresponding baseline product. It typically depends upon the application for which a product is required, which product is best to use as methods involved in producing them typically have varying strength and weaknesses. For further information on the products, such as details on the SCIAMACHY and TANSO instruments, the algorithms used to generate the data and the data's format, please see the Product Specification Document (PSD) in the documentation section.

The CH4_SCI_IMAP dataset is comprised of level 2, column-averaged dry-air mole fractions (mixing ratios) of methane (CH4). It has been produced using data acquired from the SWIR spectra (channel 6) of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's (ESA's) environmental research satellite ENVISAT using the IMAP-DOAS algorithm. It has been generated as part of ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the dataset is v7.2 and forms part of the Climate Research Data Package 4. The IMAP-DOAS algorithm has been developed at the University of Heidelberg and SRON, and has been applied here to the SCIAMACHY data. This procedure and the algorithms validity are thoroughly described in Frankenberg et al (2011). A second product is also available which has been generated using the Weighting Function Modified DOAS (WFM-DOAS) algorithm. The data product is stored per orbit in a single NetCDF4 file. Retrieval results are provided for the individual SCIAMACHY spatial footprints, no averaging having been applied. The product file contains the key products and information relevant to using the data, such as the vertical layering and averaging kernels. For further details on the product, including the IMAP algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Document.

The CO2_SCI_WFMD dataset comprises level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2) from the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. It has been produced using the Weighting Function Modified DOAS (WFM-DOAS) algorithm, by the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. The WFM-DOAS algorithm is a least-squares method based on scaling pre-selected atmospheric vertical profiles. Note that this has been designated as an 'alternative' algorithm for the GHG_cci and another XCO2 product has also been generated from the SCIAMACHY data using the baseline algorithm (the Bremen Optimal Estimation DOAS (BESD) algorithm). It is advised that users who aren't sure whether to use the baseline or alternative product use the product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage. The data product is stored per day in seperate NetCDF-files (NetCDF-4 classic model). The product files contain the key products, i.e. the retrieved column-averaged dry air mole fractions for XCO2, several other useful parameters and additional information relevant to using the data e.g. the averaging kernels. For further information on the product, including details of the WFMD algorithm, the SCIAMACHY instrument and issues associated with the data please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents in the documentation section.

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.

Part of the European Space Agency's (ESA) Greenhouse Gases (GHG) Climate Change Initiative (CCI) project and the Climate Research Data Package Number 3 (CRDP#3), the BESD XCO2 SCIAMACHY 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 SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This product has been produced with the Bremen Optimal Estimation DOAS (BESD) algorithm, a full physics algorithm which uses measurements in the O2-A absorption band to retrieve scattering information of clouds and aerosols. This is the GHG CCI baseline algorithm for deriving SCIAMACHY XCO2 data: A product has also been generated from the SCIAMACHY data using an alternative algorithm: the WFMD algorithm. It is advised that users who aren't sure whether to use the baseline or alternative product use this product generated with the BESD baseline algorithm. For more information regarding the differences between baseline and alternative algorithms please see the GHG-CCI data products webpage in the documentation section. For further information on the product, including details of the BESD algorithm and the SCIAMACHY instrument, please see the associated product user guide (PUG) or the Algorithm Theoretical Basis Documents 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 CO2_EMMA dataset comprises of level 2, column-averaged dry-air mole fractions (mixing ratios) of carbon dioxide (XCO2). It has been produced using the ensample median algorithm EMMA to produce a merged SCIAMACHY and GOSAT XCO2 Level 2 product, as part of the ESA Greenhouse Gases Climate Change Initiative (GHG_cci) project. This version of the product is v2.2, and forms part of the Climate Research Data Package 4. The EMMA algorithm has been applied to level 2 data from multiple XCO2 retrievals from the Japanese Greenhouse gases Observing Satellite (GOSAT) and the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) on board the European Space Agency's environmental research satellite ENVISAT. This merged SCIAMACHY and GOSAT XCO2 Level 2 product 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.

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 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.