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| Titel |
Expanding the validity of the ensemble Kalman filter without the intrinsic need for inflation |
| VerfasserIn |
M. Bocquet, P. N. Raanes, A. Hannart |
| 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 (2015-11-03), S.645-662 |
| Datensatznummer |
250121007
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| Publikation (Nr.) |
 copernicus.org/npg-22-645-2015.pdf |
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| Zusammenfassung |
| The ensemble Kalman filter (EnKF) is a powerful data assimilation method
meant for high-dimensional nonlinear systems. But its implementation requires
somewhat ad hoc procedures such as localization and inflation. The recently
developed finite-size ensemble Kalman filter (EnKF-N) does not
require multiplicative inflation meant to counteract sampling errors. Aside
from the practical interest in avoiding the tuning of inflation in perfect
model data assimilation experiments, it also offers theoretical insights and
a unique perspective on the EnKF. Here, we revisit, clarify and correct
several key points of the EnKF-N derivation. This simplifies the use of the
method, and expands its validity. The EnKF is shown to not only rely on the
observations and the forecast ensemble, but also on an implicit prior
assumption, termed hyperprior, that fills in the gap of missing
information. In the EnKF-N framework, this assumption is made explicit
through a Bayesian hierarchy. This hyperprior has so far been chosen to be
the uninformative Jeffreys prior. Here, this choice is revisited to
improve the performance of the EnKF-N in the regime where the analysis is
strongly dominated by the prior. Moreover, it is shown that the EnKF-N can be
extended with a normal-inverse Wishart informative hyperprior that introduces
additional information on error statistics. This can be identified as a
hybrid EnKF–3D-Var counterpart to the EnKF-N. |
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