Repository: Freie Universit├Ąt Berlin, Math Department

A HYBRID ENSEMBLE TRANSFORM FILTER FOR HIGH DIMENSIONAL DYNAMICAL SYSTEMS

Chustagulprom, N. and Reich, S. and Reinhardt, M. (2016) A HYBRID ENSEMBLE TRANSFORM FILTER FOR HIGH DIMENSIONAL DYNAMICAL SYSTEMS. SIAM/ASA Journal on Uncertainty Quantification, 4 (1). pp. 592-608. ISSN 2166-2525

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Official URL: http://epubs.siam.org/doi/abs/10.1137/15M1040967

Abstract

Data assimilation is the task to combine evolution models and observational data in order to produce reliable predictions. In this paper, we focus on ensemble-based recursive data assimilation problems. Our main contribution is a hybrid filter that allows one to adaptively bridge between ensemble Kalman and particle filters. While ensemble Kalman filters are robust and applicable to high dimensional problems, particle filters are asymptotically consistent in the large ensemble size limit. We demonstrate numerically that our hybrid approach can improve the performance of both Kalman and particle filters at moderate ensemble sizes. We also show how to implement the concept of localisation into a hybrid filter, which is key to their applicability to high dimensional problems.

Item Type:Article
Uncontrolled Keywords:Bayesian inference, Monte Carlo method, sequential data assimilation, ensemble Kalman filter, particle filter, localisation, dynamical systems
Subjects:Mathematical and Computer Sciences > Mathematics > Applied Mathematics
ID Code:1735
Deposited By: Ulrike Eickers
Deposited On:30 Sep 2015 14:44
Last Modified:21 Apr 2017 13:11

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