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This technical report discusses the development of efficient Ensemble Kalman Filter (EnKF) implementations utilizing shrinkage covariance estimation, specifically the Rao-Blackwell Ledoit and Wolf (RBLW) estimator. The proposed methods aim to improve the estimation of background error covariance matrices in high-dimensional settings, outperforming traditional EnKF methods with smaller ensemble sizes. Experimental results demonstrate that these implementations maintain low computational costs while achieving

Author

Juan Martín Guerrieri

Language

EN