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| Titel |
Sequential parameter estimation for stochastic systems |
| VerfasserIn |
G. A. Kivman |
| 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. 3 ; Nr. 10, no. 3, S.253-259 |
| Datensatznummer |
250007994
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| Publikation (Nr.) |
 copernicus.org/npg-10-253-2003.pdf |
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| Zusammenfassung |
| The quality of the
prediction of dynamical system evolution is determined by the accuracy to
which initial conditions and forcing are known. Availability of future
observations permits reducing the effects of errors in assessment the
external model parameters by means of a filtering algorithm. Usually,
uncertainties in specifying internal model parameters describing the inner
system dynamics are neglected. Since they are characterized by strongly
non-Gaussian distributions (parameters are positive, as a rule),
traditional Kalman filtering schemes are badly suited to reducing the
contribution of this type of uncertainties to the forecast errors. An
extension of the Sequential Importance Resampling filter (SIR) is proposed
to this aim. The filter is verified against the Ensemble Kalman filter (EnKF)
in application to the stochastic Lorenz system. It is shown that the SIR
is capable of estimating the system parameters and to predict the
evolution of the system with a remarkably better accuracy than the EnKF.
This highlights a severe drawback of any Kalman filtering scheme: due to
utilizing only first two statistical moments in the analysis step it is
unable to deal with probability density functions badly approximated by
the normal distribution. |
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