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Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model
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Yuting Zhang1, Zheqi Shen1, 2, 3, *, Yanling Wu1, 2, 3
Haiyang Xuebao | 2021, 43(10) : 137 - 148
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Haiyang Xuebao | 2021, 43(10): 137-148
• Article •
Data assimilation experiments using localized particle filter and ensemble Kalman filter with community earth system model
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Yuting Zhang1, Zheqi Shen1, 2, 3, *, Yanling Wu1, 2, 3
Affiliations
  • 1State Key Laboratory of Satellite Marine Environmental Dynamics, Second Institute of Oceanology, Ministry of Natural Resources, Hangzhou 310012, China
  • 2Institute of Data Assimilation and Prediction, School of Oceanography, Hohai University, Nanjing 210098, China
  • 3Guangdong Laboratory of Southern Ocean Science and Engineering (Zhuhai), Zhuhai 519080, China
Published: 2021-10-25 doi: 10.12284/hyxb2021139
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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
Yuting Zhang, Zheqi Shen, Yanling Wu. 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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Year 2021 volume 43 Issue 10
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Article Info
doi: 10.12284/hyxb2021139
  • Receive Date：2020-07-28
  • Online Date：2026-02-26
  • Published：2021-10-25
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History
  • Received：2020-07-28
  • Revised：2021-01-14
Funding
Affiliations
    1State Key Laboratory of Satellite Marine Environmental Dynamics, Second Institute of Oceanology, Ministry of Natural Resources, Hangzhou 310012, China
    2Institute of Data Assimilation and Prediction, School of Oceanography, Hohai University, Nanjing 210098, China
    3Guangdong Laboratory of Southern Ocean Science and Engineering (Zhuhai), Zhuhai 519080, China
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多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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