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 National Centre for Earth Observation (NCEO): Monthly global Particulate Organic Carbon (POC) dataset contains POC concentrations gridded on both sinusoidal (SIN) and geographic (GEO) grid projections at 4 km spatial resolution for 1997-2020. The POC dataset has been produced using the Ocean Colour Climate Change Initiative Remote Sensing Reflectance (Rrs) products, Version 4.2. The dataset includes the Rrs at 443 nm and 555 nm with pixel-by-pixel uncertainty estimates for each wavelength. For more details on the algorithm and its validation, please see papers by Stramski et al. (2008) and Evers-King et al. (2017). Please note that the validation of the POC algorithm is a continuing process. To increase the accuracy of POC algorithms, further in situ POC data need to be collected with high spatial and temporal resolution.