Geosciences Research

Download PDF (412.2 KB) PP. 1 - 5 Pub. Date: February 10, 2017

DOI: 10.22606/gr.2017.21001

• Jiasheng Tian^*
  School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan city, Hubei Province , China
• Hang Xue
  School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan city, Hubei Province , China
• Jian Shi
  School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan city, Hubei Province , China

The neural network algorithm in this paper is applied to the ocean surface wind speed retrievals. Firstly, the Ku band backscattering coefficient (σ⁰_Ku) is considered as the input parameter to retrieve the wind speed and the retrieval precision reaches 1 m/s (root mean square error) for HY2 altimeter. Secondly, by introducing the Ku-band significant wave height (swh_ku), C-band backscattering coefficients (σ⁰_C) and C-band swh (swh_C) as input parameters to inverse wind speed, the retrieved results show that the multi-parameter algorithm introduced in the neural network can effectively improve the wind speed retrieval accuracy. The wind speed is not only relative to σ⁰_Ku, but also to σ⁰_C, swhku and swh_C. The neural networks algorithm is available for HY2 altimeter wind speed retrieval.

Neural network, wind speed retrieval, backscatter coefficient, multi-parameter algorithm.

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