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| Titel |
Estimating model error covariance matrix parameters in extended Kalman filtering |
| VerfasserIn |
A. Solonen, J. Hakkarainen, A. Ilin, M. Abbas, A. Bibov |
| 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 ; 21, no. 5 ; Nr. 21, no. 5 (2014-09-01), S.919-927 |
| Datensatznummer |
250120938
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| Publikation (Nr.) |
 copernicus.org/npg-21-919-2014.pdf |
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| Zusammenfassung |
| The extended Kalman filter (EKF) is a popular
state estimation method for nonlinear dynamical models. The model error
covariance matrix is often seen as a tuning parameter in EKF, which is often
simply postulated by the user. In this paper, we study the filter likelihood
technique for estimating the parameters of the model error covariance matrix.
The approach is based on computing the likelihood of the covariance matrix
parameters using the filtering output. We show that (a) the importance of the
model error covariance matrix calibration depends on the quality of the
observations, and that (b) the estimation approach yields a well-tuned EKF in
terms of the accuracy of the state estimates and model predictions. For our
numerical experiments, we use the two-layer quasi-geostrophic model that is
often used as a benchmark model for numerical weather prediction. |
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