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DOI | 10.1002/2017JD027999 |
An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods | |
Zhang, Hongqin1,2; Tian, Xiangjun1,2,3 | |
2018-04-16 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES
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ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:7页码:3556-3573 |
文章类型 | Article |
语种 | 英语 |
国家 | Peoples R China |
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 (B-e). Extensive research has been undertaken to reduce the computational cost of these methods by using the localized ensemble samples to localize B-e 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. |
英文关键词 | data assimilation localization ensemble correlation matrix decomposition NLS-4DVar |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000430786500013 |
WOS关键词 | VARIATIONAL DATA ASSIMILATION ; ATMOSPHERIC DATA ASSIMILATION ; DOPPLER RADAR OBSERVATIONS ; FILTER DATA ASSIMILATION ; KALMAN FILTER ; PART II ; COVARIANCE LOCALIZATION ; ERROR COVARIANCES ; REAL OBSERVATIONS ; CLOUD MODEL |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/33032 |
专题 | 气候变化 |
作者单位 | 1.Chinese Acad Sci, Inst Atmospher Phys, Int Ctr Climate & Environm Sci, Beijing, Peoples R China; 2.Univ Chinese Acad Sci, Beijing, Peoples R China; 3.Nanjing Univ Informat Sci & Technol, Collaborat Innovat Ctr Forecast & Evaluat Meteoro, Nanjing, Jiangsu, Peoples R China |
推荐引用方式 GB/T 7714 | Zhang, Hongqin,Tian, Xiangjun. An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2018,123(7):3556-3573. |
APA | Zhang, Hongqin,&Tian, Xiangjun.(2018).An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(7),3556-3573. |
MLA | Zhang, Hongqin,et al."An Efficient Local Correlation Matrix Decomposition Approach for the Localization Implementation of Ensemble-Based Assimilation Methods".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.7(2018):3556-3573. |
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