ZOU Juhong, LIN Mingsen, PAN Delu, CHEN Zhenghua, YANG Le. A unified C-band and Ku-band geophysical model function determined by neural network approach[J]. Acta Oceanologica Sinica, 2008, (6): 33-39.
Citation:
ZOU Juhong, LIN Mingsen, PAN Delu, CHEN Zhenghua, YANG Le. A unified C-band and Ku-band geophysical model function determined by neural network approach[J]. Acta Oceanologica Sinica, 2008, (6): 33-39.
ZOU Juhong, LIN Mingsen, PAN Delu, CHEN Zhenghua, YANG Le. A unified C-band and Ku-band geophysical model function determined by neural network approach[J]. Acta Oceanologica Sinica, 2008, (6): 33-39.
Citation:
ZOU Juhong, LIN Mingsen, PAN Delu, CHEN Zhenghua, YANG Le. A unified C-band and Ku-band geophysical model function determined by neural network approach[J]. Acta Oceanologica Sinica, 2008, (6): 33-39.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;Shanghai Institute of Technical Physics, Chinese Academy of Sciences, Shanghai 200083, China
2.
National Satellite Ocean Application Center, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China
3.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
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 σ0predicted 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.
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