Combined Infrared and Microwave Retrievals of Atmospheric Profiles in the Presence of Clouds using Nonlinear Stochastic Methods: The SCENE Algorithm

Abstract

A nonlinear stochastic method for the retrieval of atmospheric temperature and moisture profiles has been devel- oped and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sound- ing Unit (AMSU), and is presently being adapted for use with the NPOESS Cross-track Infrared Microwave Sounding Suite (CrIMSS) consisting of the Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). The algorithm is implemented in three stages, motivating the name, SCENE (Stochastic Cloud clearing (1), followed by Eigenvector radiance compression and denoising, followed by Neural network Estimation). First, the infrared radiance perturbations due to clouds are estimated and corrected by combined processing of the infrared and microwave data. Second, a Projected Principal Components (PPC) transform (2) is used to reduce the dimen- sionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, an artificial feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. This paper has two major components. First, details of the SCENE algorithm are discussed, including both the architectural implementation and parameter selection and optimization. Sec- ond, the performance of the SCENE algorithm is compared with that of the AIRS Level 2 algorithm (version 4.0.9) (3) currently being used for the Aqua mission. The performance of the SCENE algorithm was evaluated using global, ascending EOS-Aqua orbits colocated with ECMWF fore- casts (generated every three hours on a 0.5-degree lat/lon grid) for a variety of days throughout 2002 and 2003. Over 300,000 fields of regard (3x3 arrays of footprints) over ocean were used in the study. The RMS temperature and moisture profile retrieval errors for the SCENE algorithm were compared to those of the AIRS Level 2 algorithm, and the performance of the SCENE algorithm exceeded that of the AIRS Level 2 algorithm throughout most of the troposphere. The SCENE algorithm requires significantly less computation than traditional variational retrieval methods while achieving comparable performance, thus the algorithm is particularly suitable for quick-look retrieval generation for post- launch CrIMSS performance validation.