四川大学学报（自然科学版）2025，Vol.62Issue(2)：334-339,6.DOI:10.19907/j.0490-6756.230395
一种基于随机协方差局部化的ETKF数据同化方法
An ETKF assimilation method based on the random covariance localization
摘要
Abstract
Data assimilation is a set of statistical methods.Based on the data distribution,observations and background errors,a data assimilation method integrates new observational data into the dynamic operation of numerical model and thus improve the estimate accuracy of transient state of system.Nowadays data assimila-tion methods are utilized in diverse research fields such as earth system science.The ensemble transform Kal-man(EnKF)filter is a typical data assimilation method.In EnKF the covariance estimation of errors is cru-cial.Insufficient number of ensembles can introduce the pseudo-correlation problem into the estimation of er-ror covariance matrix,thus results in the filtering divergence problem.By using the covariance localization(CL)and local analysis(AL)methods,the pseudo-correlation problem can be eliminated,where the CL method is not suitable for the ensemble transform Kalman filter(ETKF).To overcome this problem the ap-proximate CL method is proposed with high computation complexity.In this paper,we introduce the random covariance localization(RCL)method for ETKF.Comparison is done on the error covariance matrix,the en-semble transformation matrix and the performance of the RCL and LA methods.Two assimilation algorithms are constructed by applying the RCL and LA methods to ETKF,respectively.Numerical examples are given to show the performance of two algorithms.It is shown that the former can solve the pseudo-correlation prob-lem.On the other hand,the two algorithms hold their own characteristics for different types of model.In the case of linear advection model,the assimilation performance of the former is slightly lower than that of the lat-ter,but with more strong robustness.In the case of nonlinear Lorenz model of three variables,the assimila-tion performance of the former is significant better than that of the latter with the decrease of observation er-rors,but with some loss of robustness.关键词
数据同化/集合变换卡尔曼滤波/协方差局部化/局部分析Key words
Data assimilation/Ensemble transformation Kalman filter/Covariance localization/Local analy-sis分类
数理科学引用本文复制引用
陈丽,宋恩彬..一种基于随机协方差局部化的ETKF数据同化方法[J].四川大学学报（自然科学版）,2025,62(2):334-339,6.基金项目
国家自然科学基金(U2066203) （U2066203）
中央高校基本业务科研费专项资金资助项目(QJ2023-038) （QJ2023-038）
四川省自然科学基金项目(2025ZNSFSC0073) （2025ZNSFSC0073）
