Blending satellite scatterometer data based on variational with multi-parameter regularization method

Zhang Kaifeng; Deng Wanyue; Wang Ting; Wang Huipeng; Xiang Jie; Song Qingtao; Liu Chunxia

doi:10.3969/j.issn.0253-4193.2017.12.012

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Article Navigation > Haiyang Xuebao > 2017 > 39(12): 122-135

Zhang Kaifeng, Deng Wanyue, Wang Ting, Wang Huipeng, Xiang Jie, Song Qingtao, Liu Chunxia. Blending satellite scatterometer data based on variational with multi-parameter regularization method[J]. Haiyang Xuebao, 2017, 39(12): 122-135. doi: 10.3969/j.issn.0253-4193.2017.12.012

Citation:

Zhang Kaifeng, Deng Wanyue, Wang Ting, Wang Huipeng, Xiang Jie, Song Qingtao, Liu Chunxia. Blending satellite scatterometer data based on variational with multi-parameter regularization method[J]. Haiyang Xuebao, 2017, 39(12): 122-135. doi: 10.3969/j.issn.0253-4193.2017.12.012

Citation:

Zhang Kaifeng, Deng Wanyue, Wang Ting, Wang Huipeng, Xiang Jie, Song Qingtao, Liu Chunxia. Blending satellite scatterometer data based on variational with multi-parameter regularization method[J]. Haiyang Xuebao, 2017, 39(12): 122-135. doi: 10.3969/j.issn.0253-4193.2017.12.012

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Blending satellite scatterometer data based on variational with multi-parameter regularization method

doi: 10.3969/j.issn.0253-4193.2017.12.012

1.
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China
2.
Xinjiang Uygur Autonomous Region Environmental Monitoring Station, Urumqi 830011, China
3.
Unit No. 75839 of PLA, Guangzhou 510510, China
4.
Institute of Meteorology and Oceanography, PLA University of Science and Technology, Nanjing 211101, China;Key Laboratory of Mesoscale Severe Weather (Nanjing University), Ministry of Education, Nanjing 210023, China
5.
National Satellite Ocean Application Service, State Oceanic Administration, Beijing 100081, China
6.
Guangzhou Institute of Tropical and Marine Meteorology, China Meteorological Administration, Guangzhou 510080, China

Received Date: 2017-01-23
Rev Recd Date: 2017-03-16

Abstract

Abstract

A 3DVAR method with regularization constraints is proposed to blend sea surface wind data in the South China Sea based on the traditional 3DVAR and regularization technology of the inverse problem, and the model function method which is used to determine the reasonable regularization parameters and then the blended experiments of the satellite scatterometer (QuikSCAT) and Guang Zhou Mesoscale Model (GZMM) sea surface wind field data are carried out for a typhoon case. Results show that when we use the regularization method for experiments, the false information caused by the traditional 3DVAR is eliminated obviously and the noise is almost disappeared, at the same time, the wind field and vorticity field as well as divergence field are distributed evenly, and the structure is clear, more importantly, it is clear that the cyclone center is remarkable, and observation is dramatic in the analysis field. Besides, the degrees of freedom for signal (DFS) method is used to evaluate blended systems quantitatively, it is found that the regularized constraint 3DVAR system has a higher DFS and observation influence related to traditional 3DVAR. The blended results are tested based on the independent observation data, it indicates that the result of regularized constraint 3DVAR method has the smallest root mean square error and maximum correlation coefficient, which is better than the statistical result of GZMM and the conventional 3DVAR method.
- 3DVAR,
- regularization,
- QuikSCAT,
- the degrees of freedom for signal,
- model function

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References(27)

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