globalchange  > 气候减缓与适应
DOI: 10.1002/2017JD027999
Scopus记录号: 2-s2.0-85045839323
论文题名:
An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods
作者: Zhang H.; Tian X.
刊名: Journal of Geophysical Research: Atmospheres
ISSN: 2169897X
出版年: 2018
卷: 123, 期:7
起始页码: 3556
结束页码: 3573
语种: 英语
英文关键词: correlation matrix ; data assimilation ; decomposition ; ensemble ; localization ; NLS-4DVar
Scopus关键词: computer simulation ; covariance analysis ; data assimilation ; decomposition analysis ; error analysis ; experimental study ; interpolation ; least squares method ; nonlinearity
英文摘要: Ensemble-based data assimilation methods often use the so-called localization scheme to improve the representation of the ensemble background error covariance (Be). Extensive research has been undertaken to reduce the computational cost of these methods by using the localized ensemble samples to localize Be by means of a direct decomposition of the local correlation matrix C. However, the computational costs of the direct decomposition of the local correlation matrix C are still extremely high due to its high dimension. In this paper, we propose an efficient local correlation matrix decomposition approach based on the concept of alternating directions. This approach is intended to avoid direct decomposition of the correlation matrix. Instead, we first decompose the correlation matrix into 1-D correlation matrices in the three coordinate directions, then construct their empirical orthogonal function decomposition at low resolution. This procedure is followed by the 1-D spline interpolation process to transform the above decompositions to the high-resolution grid. Finally, an efficient correlation matrix decomposition is achieved by computing the very similar Kronecker product. We conducted a series of comparison experiments to illustrate the validity and accuracy of the proposed local correlation matrix decomposition approach. The effectiveness of the proposed correlation matrix decomposition approach and its efficient localization implementation of the nonlinear least-squares four-dimensional variational assimilation are further demonstrated by several groups of numerical experiments based on the Advanced Research Weather Research and Forecasting model. ©2018. The Authors.
Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/114099
Appears in Collections:气候减缓与适应

Files in This Item:

There are no files associated with this item.


作者单位: International Center for Climate and Environment Sciences, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China; University of Chinese Academy of Sciences, Beijing, China; Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters, Nanjing University of Information Science and Technology, Nanjing, China

Recommended Citation:
Zhang H.,Tian X.. An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods[J]. Journal of Geophysical Research: Atmospheres,2018-01-01,123(7)
Service
Recommend this item
Sava as my favorate item
Show this item's statistics
Export Endnote File
Google Scholar
Similar articles in Google Scholar
[Zhang H.]'s Articles
[Tian X.]'s Articles
百度学术
Similar articles in Baidu Scholar
[Zhang H.]'s Articles
[Tian X.]'s Articles
CSDL cross search
Similar articles in CSDL Cross Search
[Zhang H.]‘s Articles
[Tian X.]‘s Articles
Related Copyright Policies
Null
收藏/分享
所有评论 (0)
暂无评论
 

Items in IR are protected by copyright, with all rights reserved, unless otherwise indicated.