图书馆订阅: Guest
Begell Digital Portal Begell 数字图书馆 电子图书 期刊 参考文献及会议录 研究收集
国际不确定性的量化期刊
影响因子: 3.259 5年影响因子: 2.547 SJR: 0.531 SNIP: 0.8 CiteScore™: 1.52

ISSN 打印: 2152-5080
ISSN 在线: 2152-5099

Open Access

国际不确定性的量化期刊

DOI: 10.1615/Int.J.UncertaintyQuantification.2015009880
pages 491-510

AN ERROR SUBSPACE PERSPECTIVE ON DATA ASSIMILATION

Adrian Sandu
Computational Science Laboratory, Department of Computer Science, Virginia Polytechnic Institute and State University, 2201 Knowledgeworks II, 2202 Kraft Drive, Blacksburg, Virginia 24060, USA
Haiyan Cheng
Department of Computer Science, Willamette University, 900 State Street, Salem, Oregon 97301, USA

ABSTRACT

Two families of methods are widely used in data assimilation: the four-dimensional variational (4D-Var) approach, and the ensemble Kalman filter (EnKF) approach. The two families have been developed largely through parallel research efforts. Each method has its advantages and disadvantages. It is of interest to develop hybrid data assimilation algorithms that can combine the relative strengths of the two approaches. This paper proposes a subspace approach to investigate the theoretical equivalence between the suboptimal 4D-Var method (where only a small number of optimization iterations are performed) and the practical EnKF method (where only a small number of ensemble members are used) in a linear setting. The analysis motivates a new hybrid algorithm: the optimization directions obtained from a short window 4D-Var run are used to construct the EnKF initial ensemble. The proposed hybrid method is computationally less expensive than a full 4D-Var, as only short assimilation windows are considered. The hybrid method has the potential to perform better than the regular EnKF due to its look-ahead property. Numerical results show that the proposed hybrid ensemble filter method performs better than the regular EnKF method for the test problem considered.


Articles with similar content:

Analysis of a Convergence for Iterative Procedures on the Basis of Methods of Practical Stability
Journal of Automation and Information Sciences, Vol.31, 1999, issue 7-9
Alexander N. Bashnyakov, Fedor G. Garashchenko
Analysis and Estimation of Parametric Systems on the Basis of Practical Stability Methods
Journal of Automation and Information Sciences, Vol.30, 1998, issue 1
Fedor G. Garashchenko, L. A. Pantalienko
OPTIMIZATION-BASED SAMPLING IN ENSEMBLE KALMAN FILTERING
International Journal for Uncertainty Quantification, Vol.4, 2014, issue 4
Alexander Bibov, Heikki Haario, Antti Solonen, Johnathan M. Bardsley
NUMERICAL APPROXIMATION OF ELLIPTIC PROBLEMS WITH LOG-NORMAL RANDOM COEFFICIENTS
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 2
Xiaoliang Wan, Haijun Yu
A GENERAL FRAMEWORK FOR ENHANCING SPARSITY OF GENERALIZED POLYNOMIAL CHAOS EXPANSIONS
International Journal for Uncertainty Quantification, Vol.9, 2019, issue 3
Xiaoliang Wan, Huan Lei, Xiu Yang, Lin Lin