Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model

Zhang Yuting; Shen Zheqi; Wu Yanling

doi:10.12284/hyxb2021139

Volume 43 Issue 10

Oct. 2021

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Zhang Yuting，Shen Zheqi，Wu Yanling. Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model[J]. Haiyang Xuebao，2021, 43(10)：137–148 doi: 10.12284/hyxb2021139

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Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model

doi: 10.12284/hyxb2021139

Zhang Yuting^1
,,
Shen Zheqi^{1, 2, 3
,
,},
Wu Yanling^{1, 2, 3}

1.
State Key Laboratory of Satellite Marine Environmental Dynamics, Second Institute of Oceanology, Ministry of Natural Resources, Hangzhou 310012, China
2.
Institute of Data Assimilation and Prediction, School of Oceanography, Hohai University, Nanjing 210098, China
3.
Guangdong Laboratory of Southern Ocean Science and Engineering (Zhuhai), Zhuhai 519080, China

Received Date: 2020-07-28
Rev Recd Date: 2021-01-14

Available Online: 2021-06-02

Publish Date: 2021-10-30

Abstract

Abstract

Particle filter (PF) is a very promising nonlinear data assimilation method. However, due to the particle degeneracy problem, it has not been widely used in large geophysical models. In contrast, the ensemble Kalman filter (EnKF) and its derivative methods have been widely used in operational data assimilation systems in recent years. A newly proposed local particle filter (LPF) which employs the localization technique in particle filter, can effectively avoid the degeneracy problem with low computational costs and has great potential for practical applications. In this paper, data assimilation experiments using LPF and EnKF are conducted in a fully coupled Community earth system model. The sythetic satellite sea surface temperature data are assimilated with each method. Different impact of local parameters on each method is investigated, and the data assimilation performances of LPF and EnKF are compared. The comparison results show that the performance of LPF is more sensitive to localization parameter. With the optimal localization strategy, it is shown that LPF can be better than EnKF, and have a potential to be further improved.
- data assimilation,
- localized particle filter,
- ensemble Kalman filter,
- community earth system model,
- localization

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References(24)

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