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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 |
1023-5809
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics ; 22, no. 6 ; Nr. 22, no. 6 (2015-12-03), S.723-735 |
| Datensatznummer |
250121012
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| Publikation (Nr.) |
 copernicus.org/npg-22-723-2015.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 (element-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 that exist at the same locations 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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