| 摘要: |
| 在集合数据同化中，协方差局地化（covariance localization，CL）方法的使用存在限制。集合转换卡尔曼滤波（ensemble transform Kalman filter，ETKF）作为集合平方根滤波的变种方法，是一种应用较广、计算高效的数据同化方法。本文分析了CL方法应用于ETKF方法的困难，从而改进CL方法使其可以适用于ETKF方法。另外，结合浅水方程，利用Askey函数作为多元局地化函数，提出了一种适用于多元数值模型的CL方法。通过具体实验验证，得到了较好的分析结果。 |
| 关键词: 协方差局地化 集合转换卡尔曼滤波 多元局地化函数 |
| DOI：10.11759/hykx20200321001 |
| 分类号:P732 |
| 基金项目:国家重点研发计划项目（2018YFC1406200，2016YFC1401800）；国家自然科学基金项目（41406007）；中央高校基本科研业务费专项资金（19CX05003A-5）；山东省自然科学基金面上项目（ZR2020MD060） |
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| Covariance localization in the ensemble transform Kalman filter data assimilation method |
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WANG Ji-chao1, WANG Yue1, ZANG Shao-dong1, YANG Jun-gang2, JI Yan-ju1, RUAN Zong-li1
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1.College of Science, China University of Petroleum(East China), Qingdao 266580, China;2.First Institute of Oceanography, Ministry of Natural Resources, Qingdao 266061, China
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| Abstract: |
| In ensemble data assimilation, the use of covariance localization (CL) methods is limited. The ensemble transform Kalman filter (ETKF), a variant of ensemble square root filters, is a widely used and computationally efficient data assimilation method. This article theoretically analyzes the difficulties of applying CL to the ETKF method and improves CL to make it more applicable to the ETKF method. Moreover, combined with a shallow water equation, a CL method suitable for the multivariate numerical model is proposed using the Askey function as the multivariate local function. Through specific experimental verification, good analysis results are obtained. |
| Key words: covariance localization ensemble transform Kalman filter multivariate localization function |
