Effects of atmospheric light scattering on spectroscopic observations of greenhouse gases from space. Part 2: Algorithm intercomparison in the GOSAT data processing for CO2 retrievals over TCCON sites
journal contributionposted on 16.12.2016, 11:14 by S. Oshchepkov, A. Bril, T. Yokota, P. O. Wennberg, N. M. Deutscher, D. Wunch, G. C. Toon, Y. Yoshida, C. W. O'Dell, D. Crisp, C. E. Miller, C. Frankenberg, A. Butz, I. Aben, S. Guerlet, O. Hasekamp, Hartmut Boesch, Austin Cogan, Robert Parker, D. Griffith, R. Macatangay, J. Notholt, R. Sussmann, M. Rettinger, V. Sherlock, J. Robinson, E. Kyro, P. Heikkinen, D. G. Feist, I. Morino, N. Kadygrov, D. Belikov, S. Maksyutov, T. Matsunaga, O. Uchino, H. Watanabe
This report is the second in a series of companion papers describing the effects of atmospheric light scattering in observations of atmospheric carbon dioxide (CO2) by the Greenhouse gases Observing SATellite (GOSAT), in orbit since 23 January 2009. Here we summarize the retrievals from six previously published algorithms; retrieving columnaveraged dry air mole fractions of CO2 (XCO2) during 22 months of operation of GOSAT from June 2009. First, we compare data products from each algorithm with ground-based remote sensing observations by Total Carbon Column Observing Network (TCCON). Our GOSAT-TCCON coincidence criteria select satellite observations within a 5 radius of 11 TCCON sites. We have compared the GOSAT-TCCON XCO2 regression slope, standard deviation, correlation and determination coefficients, and global and station-to-station biases. The best agreements with TCCON measurements were detected for NIES 02.xx and RemoTeC. Next, the impact of atmospheric light scattering on XCO2 retrievals was estimated for each data product using scan by scan retrievals of light path modification with the photon path length probability density function (PPDF) method. After a cloud pre- filtering test, approximately 25% of GOSAT soundings processed by NIES 02.xx, ACOS B2.9, and UoL-FP: 3G and 35% processed by RemoTeC were found to be contaminated by atmospheric light scattering. This study suggests that NIES 02.xx and ACOS B2.9 algorithms tend to overestimate aerosol amounts over bright surfaces, resulting in an underestimation of XCO2 for GOSAT observations. Cross-comparison between algorithms shows that ACOS B2.9 agrees best with NIES 02.xx and UoL-FP: 3G while RemoTeC XCO2 retrievals are in a best agreement with NIES PPDF-D.