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Atmosphere 2018, 9(4), 126; https://doi.org/10.3390/atmos9040126

A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models

1
Applied Math and Computational Science Laboratory, Department of Computer Science, Universidad del Norte, Barranquilla 080001, Colombia
2
Department of Computer Science, Willamette University, 900 State Street, Salem, OR 97301, USA
*
Author to whom correspondence should be addressed.
Received: 5 January 2018 / Revised: 14 March 2018 / Accepted: 20 March 2018 / Published: 26 March 2018
(This article belongs to the Special Issue Efficient Formulation and Implementation of Data Assimilation Methods)
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Abstract

In this paper, we propose an efficient EnKF implementation for non-Gaussian data assimilation based on Gaussian Mixture Models and Markov-Chain-Monte-Carlo (MCMC) methods. The proposed method works as follows: based on an ensemble of model realizations, prior errors are estimated via a Gaussian Mixture density whose parameters are approximated by means of an Expectation Maximization method. Then, by using an iterative method, observation operators are linearized about current solutions and posterior modes are estimated via a MCMC implementation. The acceptance/rejection criterion is similar to that of the Metropolis-Hastings rule. Experimental tests are performed on the Lorenz 96 model. The results show that the proposed method can decrease prior errors by several order of magnitudes in a root-mean-square-error sense for nearly sparse or dense observational networks. View Full-Text
Keywords: ensemble Kalman filter; Gaussian Mixture Models; non-linear observation operator; Markov-Chain-Monte-Carlo ensemble Kalman filter; Gaussian Mixture Models; non-linear observation operator; Markov-Chain-Monte-Carlo
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Nino-Ruiz, E.D.; Cheng, H.; Beltran, R. A Robust Non-Gaussian Data Assimilation Method for Highly Non-Linear Models. Atmosphere 2018, 9, 126.

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