An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge

Abstract In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance matrix estimation. Our combines information brought by model realizations, that based on our prior knowledge about dynamical system interest. We perform combination both sources optimal factors. The method exploits rank-deficiency matrices to provide analysis step in EnKF formulations. Localization inflation aspects are discussed, as well. Experimental tests performed assess accuracy proposed employing Advection Diffusion Model Atmospheric General Circulation Model. experimental results reveal use can mitigate impact sampling noise, even more, it avoid spurious correlations during assimilation steps.