Abstract
A neural network (NN) algorithm for the sea surface wind speed retrieval from the MTVZA-GYa Russian satellite microwave radiometer measurements is presented. The algorithm is based on the physical modeling of the brightness temperature of microwave radiation in the ocean-atmosphere system using new theoretical geophysical model functions of the dependence of ocean radiation on wind speed. The algorithm is validated by comparing the wind fields retrieved from the MTVZA-GYa data with those obtained from the AMSR2 radiometer (Japan) for different areas of the World Ocean with a difference in measurement time not exceeding five minutes. The validation has shown that the NNs with a number of neurons n from 3 to 8 provide the smallest root-mean-square difference between the AMSR2 and MTVZA-GYa retrieved wind speeds, namely 1.6 m/s. When mapping the wind speed in tropical cyclones, the best fit to the wind fields from the AMSR2 data is obtained using the NN with n = 4.
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