Abstract: This paper presents a comparison of CO2 products derived from Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY), Greenhouse Gases Observing Satellite (GOSAT) and Atmospheric Infrared Sounder (AIRS), with reference to calibration data obtained using the high-resolution ground-based Fourier Transform Spectrometers (g-b FTS) in the Total Carbon Column Observing Network (TCCON). Based on the monthly averages, we calculate the global offsets and regional relative precisions between satellite products and g-b FTS measurements. The results are as follows: the monthly means of SCIAMACHY data are systemically slightly lower than g-b FTS, but limited in coverage; the GOSAT data are superior in stability, but inferior in systematic error; the mean difference between AIRS data and that of g-b FTS is small; and the monthly global coverage is above 95%. Therefore, the AIRS data are better than the other two satellite products in both coverage and accuracy. We also estimate linear trends based on monthly mean data and find that the differences between the satellite products and the g-b FTS data range from 0.25 ppm (SCIAMACHY) to 1.26 ppm (AIRS). The latitudinal distributions of the zonal means of the three satellite products show similar spatial features. The seasonal cycle of satellite products also illustrates the same trend with g-b FTS observations.
Keywords: greenhouse gases; remote sensing retrievals; carbon dioxide; validation
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Miao, R.; Lu, N.; Yao, L.; Zhu, Y.; Wang, J.; Sun, J. Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011). Remote Sens. 2013, 5, 3431-3456.
Miao R, Lu N, Yao L, Zhu Y, Wang J, Sun J. Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011). Remote Sensing. 2013; 5(7):3431-3456.
Miao, Ru; Lu, Ning; Yao, Ling; Zhu, Yunqiang; Wang, Juanle; Sun, Jiulin. 2013. "Multi-Year Comparison of Carbon Dioxide from Satellite Data with Ground-Based FTS Measurements (2003–2011)." Remote Sens. 5, no. 7: 3431-3456.