Effectiveness analysis of ACOS-Xco2 bias correction method with GEOS-Chem model results

Da Liu; Liping Lei; Min Liu; Lijie Guo; Qian Wang; Nian Bie

doi:10.1117/12.2204821

6 August 2015 Effectiveness analysis of ACOS-Xco₂ bias correction method with GEOS-Chem model results

Da Liu, Liping Lei, Min Liu, Lijie Guo, Qian Wang, Nian Bie

Proceedings Volume 9669, Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China; 96690E (2015) https://doi.org/10.1117/12.2204821
Event: Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China, 2014, Xian City, China

Abstract

Satellite observations and model simulations are of two important data sources to study atmospheric carbon dioxide concentration. For analyzing and evaluating the bias correction method of ACOS dry-air column averaged CO₂ (Xco₂) product, the GEOS-Chem Xco₂ simulations are selected according to observing time and locations of the ACOS product. The GEOS-Chem simulations of CO₂ profiles are transformed to Xco₂ data by convolving with satellite averaging kernels and pressure weighting functions. The GEOS-Chem Xco₂ data are then compared with both bias uncorrected and bias corrected satellite retrievals of ACOS. The comparisons show that the bias uncorrected ACOS retrievals are on average 1.12ppm higher than the model Xco₂ data, while the corrected ACOS retrievals are only on average 0.06ppm lower than the model Xco₂ data. By assuming consistency between model Xco₂ simulations and true atmospheric Xco₂, this study indicates that the bias can be obvious decreased through the bias correction method, and the correction is effective and necessary for satellite Xco₂ retrievals.

Citation Download Citation

Da Liu, Liping Lei, Min Liu, Lijie Guo, Qian Wang, and Nian Bie "Effectiveness analysis of ACOS-Xco₂ bias correction method with GEOS-Chem model results", Proc. SPIE 9669, Remote Sensing of the Environment: 19th National Symposium on Remote Sensing of China, 96690E (6 August 2015); https://doi.org/10.1117/12.2204821

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