EOF-based linear prediction algorithm: Theory

作     者：Kim, KY North, GR 

作者机构：Texas A&M Univ Climate Syst Res Program College Stn TX 77843 USA 

出 版 物：《JOURNAL OF CLIMATE》 (气候杂志)

年 卷 期：1998年第11卷第11期

页      面：3046-3056页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

主　　题：.EOFs implies covariance matrix predictand constructing insensitive available data prediction error record length Prediction Algorithm prediction kernel construction parameters prediction filter data domain 

摘      要：This study considers the theory of a general three-dimensional (space and time) statistical prediction/extrapolation algorithm. The predictor is in the form of a linear data filter. The prediction kernel is based on the minimization of prediction error and its construction requires the covariance statistics of a predict and field. The algorithm is formulated in terms of the spatiotemporal EOFs of the predict and field. This EOF representation facilitates the selection of useful physical modes for prediction. Limited tests have been conducted concerning the sensitivity of the prediction algorithm with respect to its construction parameters and the record length of available data for constructing a covariance matrix. Tests reveal that the performance of the predictor is fairly insensitive to a wide range of the construction parameters. The accuracy of the filter, however. depends strongly on the accuracy of the covariance matrix, which critically depends on the length of available data. This inaccuracy implies suboptimal performance of the prediction filter. Simple examples demonstrate the utility of the new algorithm.