| ZOU Juhong,LIN Mingsen,PAN Delu,CHEN Zhenghua,YANG Le. 2008. A unified C-band and Ku-band geophysical model function determined by neural network approach. Acta Oceanologica Sinica, (6):33－39 |
| A unified C-band and Ku-band geophysical model function determined by neural network approach |
| A unified C-band and Ku-band geophysical model function determined by neural network approach |
| Received:August 31, 2007 Revised:March 04, 2008 |
| DOI： |
| Key words:GMF neural network CMOD4 QSCAT-1 |
| 中文关键词: GMF neural network CMOD4 QSCAT-1 |
| 基金项目:This work was supported by the National Basic Research and Development Program ("973" Program), under contract No. 2009CB421202, the National Natural Science Foundation of China under contract No. 40706061 and the National High Technology Development Program ("863" Program), under contract Nos 2007AA12Z137 and 2008AA09Z104. |
| Author Name | Affiliation | E-mail | | ZOU Juhong | 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 | feeless.zou@gmail.com | | LIN Mingsen | National Satellite Ocean Application Center, National Marine Environmental Forecasting Center, State Oceanic Administration, Beijing 100081, China | | | PAN Delu | State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | | | CHEN Zhenghua | State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | | | YANG Le | State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China | |
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| Abstract: |
| 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. |
| 中文摘要: |
| 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 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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