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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。
In view of the complexity of the development mechanism of ENSO and the factors that affect the diversity of constraints, this paper, based on the SST data, the sea surface wind field and the sea surface pressure field data, Index and its related factors for time-lapse correlation analysis to find a good correlation with the dynamic factor. On the basis of this, a dynamic forecasting model of Nino3 index and its related factors is established by using the dynamical system inversion idea and the genetic algorithm approach, which overcomes the problem that the ENSO as a complicated system information insufficiency is not sufficient enough, and further improves and perfects ENSO forecast model to achieve the Nino3 index and the Southern Oscillation index, the sea surface pressure field anomalies numerical integration forecast. The experimental results show that the Nino3 index and the correlation factor forecasting model established in this paper are advanced and provide a new idea and method for the prediction of El Nino / La Nina.