DOI: 10.1002/2013MS000255
Scopus记录号: 2-s2.0-84899104886
论文题名: A practical scheme of the sigma-point Kalman filter for high-dimensional systems
作者: Tang Y ; , Deng Z ; , Manoj K ; K ; , Chen D
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2014
卷: 6, 期: 1 起始页码: 21
结束页码: 37
语种: 英语
英文关键词: Algorithms
; Equipment testing
; Kalman filters
; Assimilation algorithms
; Data assimilation
; High-dimensional models
; High-dimensional systems
; Ocean model
; Oceanic general circulation models
; Sigma-point Kalman filters
; Single value decompositions
; Mathematical models
; Argo
; data assimilation
; decomposition analysis
; ensemble forecasting
; feasibility study
; Kalman filter
; numerical model
; oceanic general circulation model
; precision
; storage structure
; Pacific Ocean
英文摘要: While applying a sigma-point Kalman filter (SPKF) to a high-dimensional system such as the oceanic general circulation model (OGCM), a major challenge is to reduce its heavy burden of storage memory and costly computation. In this study, we propose a new scheme for SPKF to address these issues. First, a reduced rank SPKF was introduced on the high-dimensional model state space using the truncated single value decomposition (TSVD) method (T-SPKF). Second, the relationship of SVDs between the model state space and a low-dimensional ensemble space is used to construct sigma points on the ensemble space (ET-SPKF). As such, this new scheme greatly reduces the demand of memory storage and computational cost and makes the SPKF method applicable to high-dimensional systems. Two numerical models are used to test and validate the ET-SPKF algorithm. The first model is the 40-variable Lorenz model, which has been a test bed of new assimilation algorithms. The second model is a realistic OGCM for the assimilation of actual observations, including Argo and in situ observations over the Pacific Ocean. The experiments show that ET-SPKF is computationally feasible for high-dimensional systems and capable of precise analyses. In particular, for realistic oceanic assimilations, the ET-SPKF algorithm can significantly improve oceanic analysis and improve ENSO prediction. A comparison between the ET-SPKF algorithm and EnKF (ensemble Kalman filter) is also tribally conducted using the OGCM and actual observations. Key Points It develops a new assimilation scheme for the earth modeling It is the first application of the SPKF on the realistic oceanic model It promotes the new assimilation method in the earth modeling ©2013. American Geophysical Union. All Rights Reserved. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/76089
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou, China; Environmental Science and Engineering, University of Northern British Columbia, Prince George BC, Canada; Department of Mathematics and Statistics, York University, Toronto ON, Canada
Recommended Citation:
Tang Y,, Deng Z,, Manoj K,et al. A practical scheme of the sigma-point Kalman filter for high-dimensional systems[J]. Journal of Advances in Modeling Earth Systems,2014-01-01,6(1)