Nino3指数及其相关因子的时延分析和混合动力模型反演  

Delay-analysis of Nino3 index and its related factors and forecasting model inversion

作　　者：洪梅[1] 张韧[1] 钱龙霞[1] 陈建[1] 

机构地区：[1]解放军理工大学气象海洋学院军事海洋教研中心,南京211101

出　　处：《水动力学研究与进展（A辑）》2013年第6期724-732,共9页Chinese Journal of Hydrodynamics

基　　金：国家自然科学基金项目(41375002;41005025/D0505;41306010);江苏省自然科学基金项目(BK2011123)~~

摘　　要：该文针对厄尔尼诺-南方涛动(ENSO)发生发展机理的复杂性和影响制约因子的多样性的问题,基于海温场、海面风场和海面气压场资料,首先采用交叉小波的分析方法对Nino3指数及其相关因子进行时滞相关分析,找出与其相关性较好的动力因子。在此基础上,采用动力系统反演思想和遗传算法途径,建立了Nino3指数及其相关因子的混合动力预报模型,克服了ENSO作为一个复杂的系统信息完备性不够充分这个问题,进一步改进完善了ENSO预报模型,实现对Nino3指数以及南方涛动指数、海面气压场距平的数值积分预报。试验结果表明,本文所建立的Nino3指数及其相关因子预报模型具有先进性,为El Nino/La Nina预测研究提供了一种新的思路和方法。Aiming to the complexity of the mechanism of the development and the diversity of restricting factor of ENSO, based on sea surface temperature （SST） field data provided by Hadley and sea surface wind （SSW） field data and sea level pressure （SLP） field data provided by NCEP / NCAR, Nino3 index and its related factors firstly have been delay-analyzed by the cross-wavelet analysis method to identify better factors. On this basis, using the dynamical system inversion idea and genetic algorithm （GA） approach, the forecasting model of Nino3 index and its related factors is established, overcoming this problem of insufficient information of ENSO as a complex system and further refining the ENSO forecast models. The numerical integration forecasts of Nino3 index, the Southern Oscillation Index and sea pressure field anomalies are carried out. The test proves that the forecasting model of the Nino3 index and its related factors is advanced and can provide a new thinking way and method of El Nino / La Nina forecast study.

关 键 词：Nino3指数 混合动力模型反演 遗传算法 ENSO预报 

分 类 号：P731.3[天文地球—海洋科学]