An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation: exploiting prior knowledge
article
In this paper, we propose an efficient and practical implementation of the ensemble Kalman filter via shrinkage covariance
matrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and that
based on our prior knowledge about the dynamical system of interest. We perform the combination of both sources of
information via optimal shrinkage factors. The method exploits the rank-deficiency of ensemble covariance matrices to
provide an efficient and practical implementation of the analysis step in EnKF based formulations. Localization and inflation
aspects are discussed, as well. Experimental tests are performed to assess the accuracy of our proposed filter implementation
by employing an Advection Diffusion Model and an Atmospheric General Circulation Model. The experimental results
reveal that the use of our proposed filter implementation can mitigate the impact of sampling noise, and even more, it can
avoid the impact of spurious correlations during assimilation steps
matrix estimation. Our filter implementation combines information brought by an ensemble of model realizations, and that
based on our prior knowledge about the dynamical system of interest. We perform the combination of both sources of
information via optimal shrinkage factors. The method exploits the rank-deficiency of ensemble covariance matrices to
provide an efficient and practical implementation of the analysis step in EnKF based formulations. Localization and inflation
aspects are discussed, as well. Experimental tests are performed to assess the accuracy of our proposed filter implementation
by employing an Advection Diffusion Model and an Atmospheric General Circulation Model. The experimental results
reveal that the use of our proposed filter implementation can mitigate the impact of sampling noise, and even more, it can
avoid the impact of spurious correlations during assimilation steps
Topics
TNO Identifier
957546
Source
Computational Geosciences, 25(June 2021), pp. 1-19.
Pages
1-19