Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

doi:10.1088/1674-1056/25/11/110502

Abstract The present work reports the development of nonlinear time series prediction method of genetic algorithm (GA) with singular spectrum analysis (SSA) for forecasting the surface wind of a point station in the South China Sea (SCS) with scatterometer observations. Before the nonlinear technique GA is used for forecasting the time series of surface wind, the SSA is applied to reduce the noise. The surface wind speed and surface wind components from scatterometer observations at three locations in the SCS have been used to develop and test the technique. The predictions have been compared with persistence forecasts in terms of root mean square error. The predicted surface wind with GA and SSA made up to four days (longer for some point station) in advance have been found to be significantly superior to those made by persistence model. This method can serve as a cost-effective alternate prediction technique for forecasting surface wind of a point station in the SCS basin.

Keywords: nonlinear time series prediction genetic algorithm surface wind prediction singular spectrum analysis

Received: 24 March 2016
Revised: 19 July 2016
Accepted manuscript online:

PACS:	05.45.Tp	(Time series analysis)
	92.60.Wc	(Weather analysis and prediction)

Fund: Project supported by the National Natural Science Foundation of China (Grant Nos. 41230421 and 41605075) and the National Basic Research Program of China (Grant No. 2013CB430101).

Corresponding Authors: Jian Zhong
E-mail: zhj_zhongjian@163.com

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Jian Zhong(钟剑), Gang Dong(董钢), Yimei Sun(孙一妹), Zhaoyang Zhang(张钊扬), Yuqin Wu(吴玉琴) Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations 2016 Chin. Phys. B 25 110502

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Application of the nonlinear time series prediction method of genetic algorithm for forecasting surface wind of point station in the South China Sea with scatterometer observations

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