A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager Export

@article{KrasnopolskyEtAl1995, abstract = {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<SUP>2</SUP> 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 m/s 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 (\~{}10\%) by including SSM/I brightness temperatures at 85 GHz. Finally, the limitations of NN algorithms are discussed in light of the present application.}, author = {Krasnopolsky, V. M. and Breaker, L. C. and Gemmill, W. H.}, citeulike-article-id = {1597803}, citeulike-linkout-0 = {http://dx.doi.org/10.1029/95JC00857}, citeulike-linkout-1 = {http://adsabs.harvard.edu/cgi-bin/nph-bib_query?bibcode=1995JGR...10011033K}, doi = {10.1029/95JC00857}, journal = {J. Geophys. Res.}, keywords = {microwave, remotesensing, wind}, month = {June}, pages = {11033--11046}, posted-at = {2007-08-28 05:18:09}, priority = {1}, title = {A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager}, url = {http://dx.doi.org/10.1029/95JC00857}, volume = {100}, year = {1995} }

TY - JOUR ID - KrasnopolskyEtAl1995 L3 - citeulike-article-id:1597803 N2 - 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<SUP>2</SUP> 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 m/s 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 (~10%) by including SSM/I brightness temperatures at 85 GHz. Finally, the limitations of NN algorithms are discussed in light of the present application. JF - J. Geophys. Res. EP - 11046 TI - A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the special sensor microwave imager VL - 100 SP - 11033 KW - microwave KW - remotesensing KW - wind AU - Krasnopolsky, VM AU - Breaker, LC AU - Gemmill, WH PY - 1995/June// UR - http://dx.doi.org/10.1029/95JC00857 ER -