The theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation

Shen Zheqi; Tang Youmin; Gao Yanqiu

doi:10.3969/j.issn.0253-4193.2016.03.001

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Shen Zheqi, Tang Youmin, Gao Yanqiu. The theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation[J]. Haiyang Xuebao, 2016, 38(3): 1-14. doi: 10.3969/j.issn.0253-4193.2016.03.001

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Shen Zheqi, Tang Youmin, Gao Yanqiu. The theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation[J]. Haiyang Xuebao, 2016, 38(3): 1-14. doi: 10.3969/j.issn.0253-4193.2016.03.001

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The theoretical framework of the ensemble-based data assimilation method and its prospect in oceanic data assimilation

doi: 10.3969/j.issn.0253-4193.2016.03.001

1.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China
2.
State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, State Oceanic Administration, Hangzhou 310012, China;Environmental Science and Engineering, University of Northern British Columbia, Prince George V2N 4Z9, Canada

Received Date: 2015-05-24
Rev Recd Date: 2015-08-07

Abstract

Abstract

In the numerical simulation of the ocean dynamic system,data assimilation is able to use the limited observation data and numerical model to best estimate the ocean state,and effectively reduce the uncertainty from the initial conditions. Therefore,data assimilation plays an important role in the study of modern physical oceanography. The ensemble Kalman filter (EnKF) is an effective data assimilation method,which has attracted broad attention in oceanic data assimilation since it is proposed about twenty years ago. In recent years,the particle filter (PF) has become a hot research field,for it is not restricted by the linear and Gaussian assumption of the model. This paper analyzes and summarizes the current theories about the EnKF and PF,in the framework of Bayesian filtering theory. The EnKF and PF algorithms are proposed and compared. On this basis,we further discuss the major obstacle for applying the particle filter in oceanic data assimilaiton at present. Some feasible solutions are also introduced. This paper is expected to provide theoretical basis for further development and application of the ensemble-based data assimilation method in oceanic data assimilation.
- data assimilation,
- ensemble Kalman filter,
- particle filter,
- non-Gaussian noise,
- Bayesian filter

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References(74)

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