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Application of the multi-parameters error model in cyclone wind retrieval with scatterometer data

Zhong Jian Fei Jian-Fang Huang Si-Xun Huang Xiao-Gang Cheng Xiao-Ping

Application of the multi-parameters error model in cyclone wind retrieval with scatterometer data

Zhong Jian, Fei Jian-Fang, Huang Si-Xun, Huang Xiao-Gang, Cheng Xiao-Ping
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Publishing process
  • Received Date:  19 March 2013
  • Accepted Date:  11 April 2013
  • Published Online:  05 August 2013

Application of the multi-parameters error model in cyclone wind retrieval with scatterometer data

  • 1. Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
Fund Project:  Project supported by the National Public Benefit (Meteorology) Research Foundation of China (Grant No. GYHY201106004), and the National Natural Science Foundation of China (Grant Nos. 41175025, 41005029, 41105012, 41105065).

Abstract: Combined with the multiple solution scheme (MSS) and the rain considered Geophysical model function (GMF+Rain), the two-dimensional variational (2DVAR) ambiguity removal technique is applied to the cyclone wind retrieval under rain condition with QuikSCAT scatterometer data. With the GMF+Rain model, the retrieved wind speed is effectively improved, but large wind direction error still exists when the background is in large error. In this paper, a changeable multi-parameter error model is introduced in the 2DVAR to reduce the wind direction error, and the sensitivity experiments of 2DVAR to its error model parameters are studied with cyclone Yagi QuikSCAT data, to choose the best parameters setting for cyclone wind retrieval with theoretical explanation. Numerical results show that 2DVAR is more effective in wind direction ambiguity removal with the proposed multi-parameter error model when the gross error probability in the multi-parameter error model is set to zero in comparison of the standard setting. The influence of the background is decreased with increasing backround error variance, decreasing the background error correlation length, or decreasing the gross error probabilities in multi-parameter error model.

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