A unified C-band and Ku-band geophysical model function determined by neural network approach

摘要

The geophysical model function (GMF) describes the relationship between backscattering and sea surface wind, so that wind vectors can be retrieved from backscattering measurement. The GMF plays an important role in ocean wind vector retrievals, its performance will directly influence the accuracy of the retrieved wind vector. Neural network (NN) approach is used to develop a unified GMF for C-band and Ku-band (NN-GMF). Empirical GMF CMOD4 and QSCAT-1 are used to generate the simulated training data-set, and Gaussian noise at a signal noise ratio of 30 dB is added to the data-set to simulate the noise in the backscattering measurement. The NN-GMF employs radio frequency as an additional parameter, so it can be applied for both C-band and Ku-band. Analyses show that the σ0 predicted by the NN-GMF is comparable with the σ0 predicted by CMOD4 and QSCAT-1. Also the wind vectors retrieved from the NN-GMF and empirical GMF CMOD4 and QSCAT-1 are comparable, indicating that the NN-GMF is as effective as the empirical GMF, and has the advantages of the universal form.