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This study developed an inversion algorithm for the CO 2 -sounder instrument to be launched on a Chinese satellite. By using the L1B spectral data of the Greenhouse gases Observing Satellite (GOSAT) as input, the simulation results of the column averaged CO 2 dry air mole fraction (XCO 2 ) retrieved from the CO 2 -sounder algorithm were obtained and compared with the GOSAT L2 XCO 2 products for different seasons and geolocations. The simulation data showed a tendency to be less than the GOSAT L2 data, but the two data sets were generally in agreement with a global mean difference of −4.05 ± 6.06 ppm. For the matched data pairs, 51% had their relative differences less than 1% in absolute value, and 76% less than 2%. The seasonal and latitudinal variations of the XCO 2 differences were also investigated, and the largest discrepancy was found in the desert areas with the relative differences of about 3%–4%. These results suggested that better consistency between the two data sets could be achieved if more realistic observational parameters instead of model values were used in the simulation. These parameters included the a priori profiles of atmospheric temperature, concentrations of CO 2 , O 2 , and H 2 O, surface reflectance, aerosol and cloud indices, etc. [ABSTRACT FROM AUTHOR]

The global carbon dioxide hyperspectral remote sensing inversion system GF_VRTM is established for the greenhouse gas carbon dioxide remote sensing detection of GF-5 satellite. The validation and error analysis of global inversion is performed by using GF_VRTM with 21 June 2013 observation data of GOSAT-FTS in this study. The simulation results show that the CO 2 averaged column concentrations (XCO 2 ) overall trends are basically identical between GF_VRTM retrievals and GOSAT-FTS observations. There are 138 exposure points in whole observation points, and the relative error less than 2% is about 85%. The mean square error is 5.00 ppm, but the global average error is 1.09 ppm. If the assumption that GOSAT-FTS observation data is true, GF_VRTM satisfies the average precision requirements (less than 1%) of XCO 2 global inversion. For the results of the comparison, the minimum error scenario and maximum error scenario are selected for error analysis. Error sources are smooth error, measurement noise error, the forward model parameter error and forward model error, respectively. For the minimum error scenario and maximum error scenario, the relative error of XCO 2 inversion caused by smooth error and measurement noise error are 0.44% and −1.62%, respectively, and the relative error of XCO 2 inversion caused by the forward model parameter error are −0.43% and −1.53%, respectively. At the same time, the forward model error is ignored. [ABSTRACT FROM AUTHOR]