Retrieval of the non-linear dynamic forecast model of El Nino/La Nina index based on the genetic algorithm optimization

被引：0

作者：

Zhang Ren ^{[1
,2
]}

Hong Mei ^{[1
,2
]}

Wang Hui-Zan ^{[1
]}

Chen Yi-De ^{[1
]}

Wang Yan-Lei ^{[1
]}

机构：

[1] PLA Univ Sci & Technol, Inst Meteorol, Nanjing 211101, Peoples R China

[2] Chinese Acad Sci, Inst Atmospher Phys, LASG, Beijing 100029, Peoples R China

来源：

CHINESE JOURNAL OF GEOPHYSICS-CHINESE EDITION | 2008年 / 51卷 / 05期

关键词：

Nino3; index; genetic algorithm; dynamic model reconstruction;

D O I：

暂无

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

Based on the 1958 similar to 1995 global month-average SSTA field reanalysis data from NCEP/NCAR, using dynamical system retrieval idea and GA Genetic Algorithm approach. the parameters of El Nino/La Nina exponential dynamical forecast model are optimized and the nonlinear dynamical model of Nino3 SSTA index is reconstructed from above SST data. The analytical result and forecast test show that GA has the advantage of global optimum search and parallel calculation. which enables objective and effective retrieval of SST index dynamical forecast model. So it can effectively forecast the Nino3 SST index and El Nino/La Nina event and afford useful reference to El Nino/La Nina forecast.

引用

页码：1346 / 1353

页数：8

共 12 条

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