海洋水文学

集合滤波和三维变分混合数据同化方法研究

  • 吴新荣 ,
  • 韩桂军 ,
  • 李冬 ,
  • 李威
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  • 1. 中国科学院南海海洋研究所 , 广东 广州 510301; 2. 国家海洋信息中心 , 天津 300171; 3. 中国科学院研究生院 , 北京 100049
吴新荣 (1981 — ), 男 , 江苏省泰州市人 , 在读博士生 , 主要从事海洋数据同化方法应用研究。

收稿日期: 2010-01-31

  修回日期: 2010-04-26

  网络出版日期: 2011-12-22

基金资助

国家重点基础研究发展计划项目 (2007CB816001); 国家自然科学 (40776016)

A hybrid ensemble filter and 3D variational analysis scheme

  • Tun-Xin-Rong ,
  • Han-Gui-Jun- ,
  • Li-Dong- ,
  • Li-Wei
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  • 1. South China Sea Institute of Oceanology, CAS, Guangzhou 510301, China ; 2. National Marine Data and Information Service, Tianjin 300171, China ; 3. Graduate University of CAS, Beijing 100049, China

Received date: 2010-01-31

  Revised date: 2010-04-26

  Online published: 2011-12-22

摘要

发展了一种新的混合数据同化方法——基于集合滤波和三维变分的混合数据同化方法。该方法将集合调整卡尔曼滤波(ensembleadjustmentKalmanfilter, EAKF)得到的集合样本扰动通过一个转换矩阵的形式直接作用到背景场上, 利用顺序滤波的思想得到分析场的一个扰动; 然后在三维变分(threedimensionalvariationalanalysis, 3D-Var)的框架下与观测数据进行拟合, 从而给出分析场的最优估计。文中以Lorenz63模型为例, 开展了理想数据同化试验, 结果表明, 相比于集合调整卡尔曼滤波, 这种新的混合同化方法可以给出更好的同化结果。

本文引用格式

吴新荣 , 韩桂军 , 李冬 , 李威 . 集合滤波和三维变分混合数据同化方法研究[J]. 热带海洋学报, 2011 , 30(6) : 24 -30 . DOI: 10.11978/j.issn.1009-5470.2011.06.024

Abstract

A new hybrid data assimilation scheme based on ensemble adjustment Kalman filter (EAKF) and three-dimensional variational (3D-Var) analysis is developed. In this assimilation scheme, the perturbation of ensemble from EAKF is applied to the background field by using a transformation matrix, thus the perturbation of the analysis field can be obtained by taking advantage of a sequential filter, which will then be optimized by being combined with observations under the framework of 3D-Var. The data assimilation experiment in a perfect case is carried out by using Lorenz-63 model. The results demonstrate that the hybrid data assimilation scheme performs better than EAKF.

参考文献

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