Neural Network Estimation of Atmospheric Profiles Using AIRS/IASI/AMSU Data in the Presence of Clouds

Abstract

A novel statistical method for the retrieval of atmospheric temperature and water vapor profiles has been developed and evaluated with sounding data from the Atmospheric InfraRed Sounder (AIRS) and the Advanced Microwave Sounding Unit (AMSU) on the NASA Aqua satellite and the Infrared Atmospheric Sounding Interferometer (IASI) and AMSU on the EUMETSAT MetOp-A satellite. The present work focuses on the cloud impact on the AIRS and IASI radiances and explores the use of the stochastic cloud clearing methodology together with neural network estimation. A stand-alone statistical algorithm will be presented that operates directly on cloud-impacted AIRS/AMSU and IASI/AMSU data, with no need for a physical cloud clearing process. The performance of this method was evaluated using global (ascending and descending) EOS-Aqua orbits collocated with ECMWF fields for a variety of days throughout 2003, 2004, 2005, and 2006. Over 1,000,000 fields of regard (3×3 arrays of footprints) over ocean and land were used in the study. The method requires significantly less computation than traditional variational retrieval methods, while achieving comparable performance. Retrieval accuracy will be evaluated using ECMWF atmospheric fields as ground truth. The accuracy of the neural network retrieval method will be compared to the accuracy of the AIRS Level 2 (Version 5) retrieval method.