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| Titel |
Multivariate localization methods for ensemble Kalman filtering |
| VerfasserIn |
S. Roh, M. Jun, I. Szunyogh, M. G. Genton |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 3 ; Nr. 2, no. 3 (2015-05-08), S.833-863 |
| Datensatznummer |
250115175
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| Publikation (Nr.) |
 copernicus.org/npgd-2-833-2015.pdf |
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| Zusammenfassung |
| In ensemble Kalman filtering (EnKF), the small number of ensemble
members that is feasible to use in a practical data assimilation
application leads to sampling variability of the estimates of the
background error covariances. The standard approach to reducing the
effects of this sampling variability, which has also been found to
be highly efficient in improving the performance of EnKF, is the
localization of the estimates of the covariances. One family of
localization techniques is based on taking the Schur (entry-wise)
product of the ensemble-based sample covariance matrix and
a correlation matrix whose entries are obtained by the
discretization of a distance-dependent correlation function. While
the proper definition of the localization function for a single
state variable has been extensively investigated, a rigorous
definition of the localization function for multiple state variables
has been seldom considered. This paper introduces two strategies for
the construction of localization functions for multiple state
variables. The proposed localization functions are tested by
assimilating simulated observations experiments into the bivariate
Lorenz 95 model with their help. |
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