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• W3080352670 endingPage "113163" @default.
• W3080352670 startingPage "113163" @default.
• W3080352670 abstract "In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter (EnKF) via the distribution-free Ledoit and Wolf (LW) covariance matrix estimator. Initially, we develop a tractable implementation of the LW estimator in high-dimensional probability spaces such as those found in the context of operational data assimilation. We employ this well-conditioned, full-rank covariance matrix estimator to approximate background error covariances and to mitigate the impact of spurious correlations during assimilation steps. The proposed formulation can be coupled within the EnKF framework to derive a matrix-free implementation via an iterative Woodbury formula. Experimental tests are performed by using an Atmospheric General Circulation Model. The numerical results are compared with those of the EnKF based on the Rao–Blackwell Ledoit, and Wolf covariance matrix estimator (EnKF-RBLW) wherein Gaussian assumptions are a must on prior members. The outcomes reveal that the use of the proposed filter can mitigate the impact of spurious correlations during assimilation stages, and even more, the proposed method can improve on the results of the EnKF-RBLW as a consequence of the Gaussian relaxation on prior errors." @default.
• W3080352670 created "2020-09-01" @default.
• W3080352670 creator A5037739760 @default.
• W3080352670 creator A5085510953 @default.
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• W3080352670 date "2021-03-01" @default.
• W3080352670 modified "2023-09-26" @default.
• W3080352670 title "An ensemble Kalman filter implementation based on the Ledoit and Wolf covariance matrix estimator" @default.
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• W3080352670 doi "https://doi.org/10.1016/j.cam.2020.113163" @default.
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