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| Titel |
A comparison of assimilation results from the ensemble Kalman Filter and a reduced-rank extended Kalman Filter |
| VerfasserIn |
X. Zang, P. Malanotte-Rizzoli |
| 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 ; 10, no. 6 ; Nr. 10, no. 6, S.477-491 |
| Datensatznummer |
250008204
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| Publikation (Nr.) |
 copernicus.org/npg-10-477-2003.pdf |
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| Zusammenfassung |
| The goal of this study is to compare
the performances of the ensemble Kalman filter and a reduced-rank extended
Kalman filter when applied to different dynamic regimes. Data assimilation
experiments are performed using an eddy-resolving quasi-geostrophic model
of the wind-driven ocean circulation. By changing eddy viscosity, this
model exhibits two qualitatively distinct behaviors: strongly chaotic for
the low viscosity case and quasi-periodic for the high viscosity case. In the reduced-rank extended Kalman filter algorithm,
the model is linearized with respect to the time-mean from a long model
run without assimilation, a reduced state space is obtained from a small
number (100 for the low viscosity case and 20 for the high viscosity case)
of leading empirical orthogonal functions (EOFs) derived from the long
model run without assimilation. Corrections to the forecasts are only made
in the reduced state space at the analysis time, and it is assumed that a
steady state filter exists so that a faster filter algorithm is obtained.
The ensemble Kalman filter is based on estimating the state-dependent
forecast error statistics using Monte Carlo methods. The ensemble Kalman
filter is computationally more expensive than the reduced-rank extended
Kalman filter.The results show that for strongly nonlinear case,
chaotic regime, about 32 ensemble members are sufficient to accurately
describe the non-stationary, inhomogeneous, and anisotropic structure of
the forecast error covariance and the performance of the reduced-rank
extended Kalman filter is very similar to simple optimal interpolation and
the ensemble Kalman filter greatly outperforms the reduced-rank extended
Kalman filter. For the high viscosity case, both the reduced-rank extended
Kalman filter and the ensemble Kalman filter are able to significantly
reduce the analysis error and their performances are similar. For the high
viscosity case, the model has three preferred regimes, each with distinct
energy levels. Therefore, the probability density of the system has a
multi-modal distribution and the error of the ensemble mean for the
ensemble Kalman filter using larger ensembles can be larger than with
smaller ensembles. |
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