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标题

基于观测误差调整的集合预报同化方法

作者

盛瑶 李勇

机构

北京师范大学数学科学学院

摘要

集合卡尔曼滤波对预报方差阵的估计不准,导致了滤波发散。为解决此问题,我们从状态与观测的关系 出发,提出一种判断预报误差方差阵估计是否准确的准则。在此基础上,我们构建了基于观测误差控制的一种膨 胀集合预报同化方法。数据模拟结果表明,与其他膨胀EnKF相比,这种方法能很好地克服滤波发散现象,其均方 根误差更小,其长时间估计结果更为稳定,且构造更为简单,计算效率更高,是克服EnKF滤波发散现象的理想途径。

关键词

集合卡尔曼滤波; 滤波发散; Lorenz模型; 膨胀因子

引用

赵海楠, 金蛟. 缺失数据下线性EV模型的稳健参数估计[J]. 北京师范大学学报(自然科学版), 2015, 51(1) : 5-8

基金

国家重点基础科研项目(2010CB950703)

分类号

O174.41

DOI

10.16360j.cnki.jbnuns.2015.01.003

Title

AN ENSEMEBLE FORECAST METHOD BASED ON OBSERVAYIONAL ERRORS

Author

SHENG Yao LI Yong

Affiliations

School of Mathematical Sciences, Beijing Normal University

Abstract

Ensemble Kalman filter under-estimates forecast error covariance matrix, leading to filter divergence problems. To solve this problem, criteria are proposed here to evaluate accuracy of forecast error covariance matrix based on relationship between states and observations. A new method is introduced to inflate forecast error covariance matrix and to construct an ensemble Kalman filter controlled by observational error covariance matrix. This method does not require perturbation of observations and effectively avoids filter divergence. Simulation studies show that this method has the smallest root mean square errors, and is more stable for computation over a long period of time and is easier to construct compared to other inflation methods.

Key words

Ensemble Kalman filter; filter divergence; Lorenz model; inflation factor

cite

SHENG Yao, LI Yong. AN ENSEMEBLE FORECAST METHOD BASED ON OBSERVAYIONAL ERRORS[J]. Journal of Beijing Normal University(Natural Science), 2015, 51(1) : 9-13

DOI

10.16360j.cnki.jbnuns.2015.01.003

Copyright © 2014 Journal of Beijing Normal University (Natural Science)
Designed by Mr. Sun Chumin. Email: cmsun@mail.bnu.edu.cn