An explicit four-dimensional variational data assimilation method based on the proper orthogonal decomposition: Theoretics and evaluation

被引:9
|
作者
Tian XiangJun [1 ]
Xie ZhengHui [1 ]
机构
[1] Chinese Acad Sci, Inst Atmospher Phys, Beijing 100029, Peoples R China
来源
SCIENCE IN CHINA SERIES D-EARTH SCIENCES | 2009年 / 52卷 / 02期
基金
中国国家自然科学基金; 国家高技术研究发展计划(863计划);
关键词
POD; data assimilation; 4DVAR; explicit method; PHYSICAL PROCESSES; ADJOINT; 4D-VAR;
D O I
10.1007/s11430-009-0012-4
中图分类号
P [天文学、地球科学];
学科分类号
07 ;
摘要
The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to derive the gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique significantly simplifies the data assimilation process. Several assimilation experiments show that this POD-based explicit four-dimensional variational data assimilation method performs much better than the usual ensemble Kalman filter method on both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based explicit four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from both the noise of the initial filed and the uncertainty of the forecast model.
引用
收藏
页码:279 / 286
页数:8
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