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Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 constituent
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Haowen Wu1, Yanling Zhao2, Guijun Han1, *, Wei Li1, *, Lige Cao1, Xiaobo Wu1, Chaoliang Li1, Yundong Li1, Gongfu Zhou1
Haiyang Xuebao | 2022, 44(6) : 10 - 21
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Haiyang Xuebao | 2022, 44(6): 10-21
• Article •
Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 constituent
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Haowen Wu1, Yanling Zhao2, Guijun Han1, *, Wei Li1, *, Lige Cao1, Xiaobo Wu1, Chaoliang Li1, Yundong Li1, Gongfu Zhou1
Affiliations
  • 1. School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
  • 2. The 31010 Army of PLA, Beijing 100081, China
Published: 2022-05-25 doi: 10.12284/hyxb2022057
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Data assimilation can estimate the uncertain parameters in the numerical model while adjusting the state variables with observations to improve the simulation results through enhancing the numerical model. Based on the ensemble adjustment Kalman filter (EAKF) and the external mode of the Princeton ocean model with generalized coordinate system (POMgcs), a bathymetry estimate is performed in the M2 constituent simulation of the Bohai Sea and part of the Yellow Sea. The results of the ideal data assimilation experiment or identical twin experiment show that the EAKF method can retrieve the “truth” bathymetry. In the practical data assimilation experiment of the NAO.99Jb and tide gauge data, by comparing with the 34 tide gauges, the model simulated amplitude and phase lag errors of M2 constituent are reduced by 40.27% and 49.19%, respectively, by use of the posterior estimate of the bathymetry.

data assimilation  /  EAKF  /  numerical simulation  /  Bohai Sea  /  Yellow Sea  /  M2 constituent  /  bathymetry estimation
Haowen Wu, Yanling Zhao, Guijun Han, Wei Li, Lige Cao, Xiaobo Wu, Chaoliang Li, Yundong Li, Gongfu Zhou. Bathymetry estimation using ensemble adjustment Kalman filter in the numerical simulation of M2 constituent[J]. Haiyang Xuebao, 2022 , 44 (6) : 10 -21 . DOI: 10.12284/hyxb2022057
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Year 2022 volume 44 Issue 6
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Article Info
doi: 10.12284/hyxb2022057
  • Receive Date：2021-06-22
  • Online Date：2026-02-01
  • Published：2022-05-25
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  • Received：2021-06-22
  • Revised：2021-09-03
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    1. School of Marine Science and Technology, Tianjin University, Tianjin 300072, China
    2. The 31010 Army of PLA, Beijing 100081, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
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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