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| Titel |
Combining inflation-free and iterative ensemble Kalman filters for strongly nonlinear systems |
| VerfasserIn |
M. Bocquet, P. Sakov |
| 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 ; 19, no. 3 ; Nr. 19, no. 3 (2012-06-25), S.383-399 |
| Datensatznummer |
250014215
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| Publikation (Nr.) |
 copernicus.org/npg-19-383-2012.pdf |
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| Zusammenfassung |
The finite-size ensemble Kalman filter (EnKF-N) is an
ensemble Kalman filter (EnKF) which, in perfect model condition, does
not require inflation because it partially accounts for the ensemble
sampling errors. For the Lorenz '63 and '95 toy-models, it was so far
shown to perform as well or better than the EnKF with an optimally
tuned inflation. The iterative ensemble Kalman filter (IEnKF) is an
EnKF which was shown to perform much better than the EnKF in strongly
nonlinear conditions, such as with the Lorenz '63 and '95 models, at the
cost of iteratively updating the trajectories of the ensemble members.
This article aims at further exploring the two filters and at
combining both into an EnKF that does not require
inflation in perfect model condition, and which is as efficient as the
IEnKF in very nonlinear conditions.
In this study, EnKF-N is first
introduced and a new implementation is developed. It decomposes
EnKF-N into a cheap two-step algorithm that amounts to computing an
optimal inflation factor.
This offers a justification of the use of the inflation technique
in the traditional EnKF and why it can often be efficient. Secondly, the IEnKF is
introduced following a new implementation based on the
Levenberg-Marquardt optimisation algorithm. Then, the two approaches
are combined to obtain the finite-size iterative ensemble Kalman
filter (IEnKF-N). Several numerical experiments are performed on
IEnKF-N with the Lorenz '95 model. These experiments demonstrate its
numerical efficiency as well as its performance that offer, at least,
the best of both filters. We have also selected a demanding case based
on the Lorenz '63 model that points to ways to improve the finite-size
ensemble Kalman filters. Eventually, IEnKF-N could be seen as the
first brick of an efficient ensemble Kalman smoother for strongly
nonlinear systems. |
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