| Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter |
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| 作者姓名： | Meng Shen Yan Chen Pinqiang Wang Weimin Zhang |
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| 作者单位： | College of Meteorology and Oceanology,National University of Defense Technology,Changsha 410073,China;College of Meteorology and Oceanology,National University of Defense Technology,Changsha 410073,China;Laboratory of Software Engineering for Complex Systems,Changsha 410073,China |
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| 基金项目： | The National Key Research and Development Program of China under contract No.2018YFC1406202;the National Natural Science Foundation of China under contract No.41830964. |
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| 摘 要： | The Localized Weighted Ensemble Kalman Filter(LWEnKF) is a new nonlinear/non-Gaussian data assimilation(DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height(AT-SSH), swath sea surface temperature(S-SST) and in-situ temperature and salinity(T/S) profiles are assimilated using the LWEnKF in the northern South China ...
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| 关 键 词： | data assimilation Localized Weighted Ensemble Kalman Filter northern South China Sea sea surface height sea surface temperature temperature and salinity profiles mesoscale eddy |
Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter |
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| Meng Shen,Yan Chen,Pinqiang Wang,Weimin Zhang.Assimilating satellite SST/SSH and in-situ T/S profiles with the Localized Weighted Ensemble Kalman Filter[J].Acta Oceanologica Sinica,2022,41(2):26-40. |
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| Authors: | Meng Shen Yan Chen Pinqiang Wang Weimin Zhang |
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| Institution: | 1.College of Meteorology and Oceanology, National University of Defense Technology, Changsha 410073, China2.Laboratory of Software Engineering for Complex Systems, Changsha 410073, China |
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| Abstract: | The Localized Weighted Ensemble Kalman Filter (LWEnKF) is a new nonlinear/non-Gaussian data assimilation(DA) method that can effectively alleviate the filter degradation problem faced by particle filtering, and it has great prospects for applications in geophysical models. In terms of operational applications, along-track sea surface height (AT-SSH), swath sea surface temperature (S-SST) and in-situ temperature and salinity (T/S) profiles are assimilated using the LWEnKF in the northern South China Sea (SCS). To adapt to the vertical S-coordinates of the Regional Ocean Modelling System (ROMS), a vertical localization radius function is designed for T/S profiles assimilation using the LWEnKF. The results show that the LWEnKF outperforms the local particle filter (LPF) due to the introduction of the Ensemble Kalman Filter (EnKF) as a proposal density; the RMSEs of SSH and SST from the LWEnKF are comparable to the EnKF, but the RMSEs of T/S profiles reduce significantly by approximately 55％ for the T profile and 35％ for the S profile (relative to the EnKF). As a result, the LWEnKF makes more reasonable predictions of the internal ocean temperature field. In addition, the three-dimensional structures of nonlinear mesoscale eddies are better characterized when using the LWEnKF. |
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| Keywords: | data assimilation Localized Weighted Ensemble Kalman Filter northern South China Sea sea surface height sea surface temperature temperature and salinity profiles mesoscale eddy |
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