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| Titel |
A potential implicit particle method for high-dimensional systems |
| VerfasserIn |
B. Weir, R. N. Miller, Y. H. Spitz |
| 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 ; 20, no. 6 ; Nr. 20, no. 6 (2013-11-28), S.1047-1060 |
| Datensatznummer |
250086078
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| Publikation (Nr.) |
 copernicus.org/npg-20-1047-2013.pdf |
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| Zusammenfassung |
| This paper presents a particle method designed for high-dimensional state
estimation. Instead of weighing random forecasts by their distance to given
observations, the method samples an ensemble of particles around an optimal
solution based on the observations (i.e., it is implicit). It differs from
other implicit methods because it includes the state at the previous
assimilation time as part of the optimal solution (i.e., it is a lag-1
smoother). This is accomplished through the use of a mixture model for the
background distribution of the previous state. In a high-dimensional, linear,
Gaussian example, the mixture-based implicit particle smoother does not
collapse. Furthermore, using only a small number of particles, the implicit
approach is able to detect transitions in two nonlinear, multi-dimensional
generalizations of a double-well. Adding a step that trains the sampled
distribution to the target distribution prevents collapse during the
transitions, which are strongly nonlinear events. To produce similar
estimates, other approaches require many more particles. |
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