Methane retrieval from Atmospheric Infrared Sounder using EOF-based regression algorithm and its validation

Abstract

This paper presents a rapid regression algorithm for the retrieval of methane (CH4) profile from Atmospheric Infrared Sounder (AIRS) based on empirical orthogonal functions (EOF) and its validation. This algorithm was trained using the simulated radiance from an assemble of atmospheric profiles and can be utilized to derive the CH4 profile rapidly with the input of the AIRS cloud-clear radiance. Validation using hundreds of aircraft profiles demonstrates that the root mean square error (RMSE) is about 1.5 % in the AIRS sensitive region of 359–596 hPa, which is smaller than AIRS-V5 product (except in high latitudes). Comparison with the ground-based solar Fourier transform spectrometry observations showed that the RMSE of the retrieved CH4 total column amount is less than 3 %. This EOF-based regression method can be easily applied to other thermal infrared sounders for deriving CH4 and some other gases, and the derived profiles can be used as the first guess for further physical retrieval.