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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 |
2198-5634
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| Digitales Dokument |
URL |
| Erschienen |
In: Nonlinear Processes in Geophysics Discussions ; 2, no. 4 ; Nr. 2, no. 4 (2015-07-24), S.1091-1136 |
| Datensatznummer |
250115184
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| Publikation (Nr.) |
 copernicus.org/npgd-2-1091-2015.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 fixes 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 of 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 been so far 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 strongly relaxes to the prior. Moreover, it is shown that
the EnKF-N can be extended with a normal-inverse-Wishart informative
hyperprior that additionally introduces climatological error
statistics. This can be identified as a hybrid 3D-Var/EnKF
counterpart to the EnKF-N. |
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