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New algorithms for total atmospheric water vapor content (Q) and total cloud liquid water content (W) retrieval from satellite microwave radiometer data, based on neural networks (NNs) and applicable for high-latitude open-water areas, were developed. For algorithm development, a radiative transfer equation numerical integration was carried out for Special Sensor Microwave/Imager (SSM/I) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) channel characteristics for nonprecipitating conditions over the open ocean. Sets of sea surface temperatures less than 15??C, surface winds, and radiosonde (r/s) reports collected by Russian research vessels served as input data for integration. It was shown that NNs perform better than the conventional regression techniques. Q retrieval algorithms were validated both for the SSM/I and AMSR-E instruments using satellite radiometric measurements collocated in space and time with polar station r/s data. The resulting SSM/I and AMSR-E retrieval errors proved to be 1.09 and 0.90 kg/m2 correspondingly. For SSM/I Q retrievals, the algorithms were compared with the Wentz global operational algorithm. This comparison demonstrated the advantages of NN-based polar regional algorithms in comparison with the Wentz global one. The retrieval errors proved to be 1.34 and 1.90 kg/m2 ( ~ 40% worse) for the NN and Wentz algorithms correspondingly.
Geoscience and Remote Sensing, IEEE Transactions on (Volume:48 , Issue: 1 )
Date of Publication: Jan. 2010