Combined microwave and hyperspectral infrared retrievals of atmospheric profiles in the presence of clouds using nonlinear stochastic methods

A nonlinear stochastic method for the retrieval of atmospheric temperature and moisture profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU), and is presently being adapted for use with the NPOESS Cross-track Infrared Microwave Sounding Suite (CrIMSS) consisting of the hyperspectral Cross-track Infrared Sounder (CrIS) and the Advanced Technology Microwave Sounder (ATMS). The algorithm is implemented in three sequential stages: 1) stochastic cloud clearing (SCC), 2) eigenvector radiance compression and denoising, and 3) neural network (NN) 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 is used to reduce the dimensionality of and optimally extract geophysical profile information from the cloud-cleared infrared radiance data. Third, a feedforward neural network is used to estimate the desired geophysical parameters from the projected principal components. The performance of the algorithm (henceforth referred to as SCC/NN) was evaluated using global (ascending and descending) EOS-Aqua orbits co-located with ECMWF forecasts (generated every three hours on a 0.5-degree lat/lon grid) and radiosonde observations (RAOBs) for a variety of days throughout 2003 and 2004. Over 500,000 fields of regard (3times3 arrays of footprints) over ocean and land were used in the study. The performance of the SCC/NN algorithm exceeded that of the AIRS Level 2 (Version 4) algorithm throughout most of the troposphere while achieving approximately four times the yield. Furthermore, the SCC/NN performance in the lowest 1 km of the atmosphere greatly exceeds that of the AIRS Level 2 algorithm as the level of cloudiness increases. The SCC/NN algorithm requires significantly less computation than traditional variational retrieval metho- ds while achieving comparable performance, thus the algorithm is particularly suitable for quick-look retrieval generation for post-launch CrIMSS performance validation.