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

Santiago Lopez-Restrepo, Elias D. Nino-Ruiz, Luis G. Guzman-Reyes, Andres Yarce, O. L. Quintero, Nicolas Pinel, Arjo Segers, A. W. Heemink

Research output: Contribution to journalArticleScientificpeer-review

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Abstract

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.

Original languageEnglish
Pages (from-to)985-1003
Number of pages19
JournalComputational Geosciences
Volume25
Issue number3
DOIs
Publication statusPublished - 2021

Keywords

  • Air quality
  • Background error covariance matrix
  • Chemical transport model
  • Data assimilation
  • Ensemble Kalman Filter

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