A hybrid particle-ensemble Kalman filter for problems with medium nonlinearity

Citeable URL: https://scholar.colorado.edu/concern/articles/2514nm61s

Abstract

Ian Grooms, Gregor Robinson Roles Conceptualization, Data curation, Investigation, Methodology, Resources, Software, Visualization, Writing – review & editing Affiliation: Department of Applied Mathematics, University of Colorado, Boulder, CO, United States of America Abstract A hybrid particle ensemble Kalman filter is developed for problems with medium non-Gaussianity, i.e. problems where the prior is very non-Gaussian but the posterior is approximately Gaussian. First the particle filter assimilates the observations with one factor of the likelihood to produce an intermediate prior that is close to Gaussian, and then the ensemble Kalman filter completes the assimilation with the remaining factor. The authors have declared that no competing interests exist. 1 Introduction Data assimilation of high-dimensional dynamical systems routinely falls to various kinds of ensemble Kalman filters (EnKF) [1]. A variety of methods have been proposed to improve the performance of particle filters in high-dimensional problems, including implicit particle filters [9–11], the equivalent-weights particle filter [12–16], likelihood approximations [17], local particle filters [18–20] and particle filters based on kernel mappings [21] and synchronization methods [22].

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