An efficient ensemble Kalman Filter implementation via shrinkage covariance matrix estimation
| Autor: | Arjo Segers, Santiago Lopez-Restrepo, Andres Yarce, Luis G. Guzman-Reyes, Arnold Heemink, Elias D. Nino-Ruiz, Nicolás Pinel, O. L. Quintero |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
010504 meteorology & atmospheric sciences
Covariance matrix Computer science 0208 environmental biotechnology 02 engineering and technology Covariance 01 natural sciences Chemical transport model 020801 environmental engineering Computer Science Applications Background error covariance matrix Ensemble Kalman Filter Computational Mathematics Noise Data assimilation Computational Theory and Mathematics Sampling (signal processing) Filter (video) Air quality Ensemble Kalman filter Computers in Earth Sciences Spurious relationship Algorithm 0105 earth and related environmental sciences |
| Zdroj: | Computational Geosciences: modeling, simulation and data analysis, 25(3) |
| ISSN: | 1420-0597 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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