集合卡尔曼滤波数据同化在一维波动方程中的应用

简要回顾了集合卡尔曼滤波(EnKF：Ensemble Kalman Filter)数据同化方法的发展历史，并介绍了EnKF数据同化方法的基本原理，利用一维非线性波动方程进行了数值试验。EnKF数据同化方法的实现过程简单可行。避免了EKF中协方差演变方程预报过程中出现的计算不准确和关于协方差矩阵的大量数据的存储问题，最主要的是EnKF可以有效控制模式变量估计误差方差的增长，改善预报效果。

Application of Ensemble Kalman Filter Assimilation in One-Dimensional Wave Equation

A brief review is given of the development history of the Ensemble Kalman Filter (EnKF) assimilation method and its essential theory. A numerical test was conducted using the one-dimensional nonlinear wave equation. Results indicate that the method has advantages of being simple to realize and being able to avoid the computation inaccuracy occurred in the prediction process using the EKF covariance variation equation and the problem of massive data storage resulted from the covariance matrix; moreover, the EnKF can control effectively the augment of the estimated error variance for model variables and improve the prediction precision.