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MSSA-SVD典型回归模型及其用于ENSO预报的试验 被引量:12

A PREDICTION EXPERIMENT BY USING THE GENERALIZED CANONICAL MIXED REGRESSION MODEL BASED ON MSSA-SVD FOR ENSO
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摘要 文中提出了一种基于多通道奇异谱分析 (MSSA)的广义典型混合回归模式。其基本思想是 ,利用MSSA SVD提取预报因子场和预报变量场的显著耦合振荡信号 ,对它们的前几个显著典型分布型建立多元线性统计气候预报模式。经对Nino海区各季海温距平所进行的短期气候预测试验表明 ,其预报效果优于其它统计预报方案 。 A generalized Canonical Mixed Regression Model based on MSSA SVD is presened to prediction of ENSO. The MSSA is a Multichannel Singular Spectrum Analysis and the SVD is Singular Value Decomposation. The basisc idea of the method is that (1) the prominent coupled oscillation signals are segregated between the forecasted fields and the forecastor fields by using MSSA SVD method;(2) the generalized Canonical Mixed Regression Model is constructed according to the first several prominent coupled oscillation patterns for ENSO prediction. The results of statistical forecast test based on the MSSA SVD method show that the predictional model possesses more advantages and better effects than other statistical prediction methods.
出处 《气象学报》 CSCD 北大核心 2002年第3期361-369,共9页 Acta Meteorologica Sinica
基金 国家九五重中之重科技项目"我国短期气候预测系统的研究" 96 90 8 0 4 0 2 课题 河南省计委科技攻关项目
关键词 多通道奇异谱分析 典型混合回归模式 ENSO预测方法 厄尔尼诺 显著耦合振荡信号 Multichannel singular spectrum analysis, Canonical mixed regression model,ENSO prediction.
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