A maximum likelihood ensemble filter via a modified cholesky decomposition for non-gaussian data assimilation

Elias David Nino-Ruiz, Alfonso Mancilla-Herrera, Santiago Lopez-Restrepo, Olga Quintero-Montoya

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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Abstract

This paper proposes an efficient and practical implementation of the Maximum Likelihood Ensemble Filter via a Modified Cholesky decomposition (MLEF-MC). The method works as follows: via an ensemble of model realizations, a well-conditioned and full-rank square-root approximation of the background error covariance matrix is obtained. This square-root approximation serves as a control space onto which analysis increments can be computed. These are calculated via Line-Search (LS) optimization. We theoretically prove the convergence of the MLEF-MC. Experimental simulations were performed using an Atmospheric General Circulation Model (AT-GCM) and a highly nonlinear observation operator. The results reveal that the proposed method can obtain posterior error estimates within reasonable accuracies in terms of ℓ − 2 error norms. Furthermore, our analysis estimates are similar to those of the MLEF with large ensemble sizes and full observational networks.

Original languageEnglish
Article number877
Number of pages26
JournalSensors (Switzerland)
Volume20
Issue number3
DOIs
Publication statusPublished - 1 Feb 2020

Keywords

  • EnKF
  • Ensemble-based data assimilation
  • Line-search optimization
  • MLEF
  • Modified cholesky decomposition

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