A NEURAL-NETWORK AS A NONLINEAR TRANSFER-FUNCTION MODEL FOR RETRIEVING SURFACE WIND SPEEDS FROM THE SPECIAL SENSOR MICROWAVE IMAGER

A single, extended-range neural network (SER NN) has been developed to model the transfer function for special sensor microwave imager (SSM/I) surface wind speed retrievals. Applied to data sets used in previous SSM/I wind speed retrieval studies, this algorithm yields a bias of 0.05 m/s and an rms difference of 1.65 m/s, compared to buoy observations. The accuracy of the SER NN for clear (low moisture) and cloudy (higher moisture/light rain) conditions equals the accuracy of NNs trained separately for each of these cases. A new moisture retrieval criterion based on a single, physically interpretable parameter, cloud liquid water, is proposed in conjunction with the SER NN. Using this retrieval criterion, (1) a moisture retrieval threshold for cloud liquid water of 0.5 kg/m(2) was estimated, and (2) 40% of the data rejected by previous rain flags could be recovered. When the SER NN was trained using this retrieval criterion, a bias of 0.03 mis and an rms value of 1.58 m/s were obtained and only 2% of the data were rejected. Also, a slight improvement in retrieval accuracy for cloudy conditions was achieved (similar to 10%) by including SSM/I brightness temperatures at 85 GHz. Finally, the limitations of NN algorithms are discussed in light of the present application.