Neural network temperature and moisture retrieval algorithm validation for AIRS/AMSU and CrIS/ATMS

Abstract

We present comprehensive validation results for the recently introduced neural network technique for retrieving vertical profiles of atmospheric temperature and water vapor from spaceborne microwave and hyperspectral infrared sounding instruments. This technique is currently in operational use as the first guess for the NASA Atmospheric Infrared Sounder (AIRS) Science Team Version 6 retrieval algorithm. The validation incorporates a variety of data sources, independent from the algorithm training set, as ground truth, including global numerical weather analyses generated by the European Center for Medium-Range Weather Forecasts, synoptic radiosonde measurements, and radiosondes dedicated for validation. The results demonstrate significant performance improvements over the previous AIRS/advanced microwave sounding unit (AMSU) operational sounding retrievals in both retrieval error and also show comparable vertical resolution. We also present initial neural network retrieval results using measurements from the Cross-Track Infrared Sounder (CrIS) and Advanced Technology Microwave Sounder (ATMS) currently flying on the Suomi National Polar-orbiting Partnership satellite.