Open AccessAn Extension of the Ensemble Kalman Filter for Estimating the Observation Error Covariance Matrix Based on the Variational Bayes’s Method
Author(s) -
Akio Nakabayashi,
G. Ueno
Publication year - 2016
Publication title -
monthly weather review
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.862
H-Index - 179
eISSN - 1520-0493
pISSN - 0027-0644
DOI - 10.1175/mwr-d-16-0139.1
Subject(s) - kalman filter , covariance intersection , covariance , covariance matrix , ensemble kalman filter , data assimilation , hyperparameter , mathematics , algorithm , extension (predicate logic) , computer science , invariant extended kalman filter , matrix (chemical analysis) , fast kalman filter , estimation of covariance matrices , bayes' theorem , extended kalman filter , bayesian probability , artificial intelligence , statistics , physics , meteorology , materials science , composite material , programming language
This paper presents an extension of the ensemble Kalman filter (EnKF) that can simultaneously estimate the state vector and the observation error covariance matrix by using the variational Bayes’s (VB) method. In numerical experiments, this capability is examined for a time-variant observation error covariance matrix, and it is noteworthy that this method works well even when the true observation error covariance matrix is nondiagonal. In addition, two complementary studies are presented. First, the stability of a long-run assimilation is demonstrated when there are unmodeled disturbances. Second, a maximum-likelihood (ML) method is derived and demonstrated for optimizing the hyperparameters used in this method.