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| Titel |
Synchronicity in predictive modelling: a new view of data assimilation |
| VerfasserIn |
G. S. Duane, J. J. Tribbia, J. B. Weiss |
| 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 ; 13, no. 6 ; Nr. 13, no. 6 (2006-11-03), S.601-612 |
| Datensatznummer |
250011869
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| Publikation (Nr.) |
 copernicus.org/npg-13-601-2006.pdf |
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| Zusammenfassung |
| The problem of data assimilation can be viewed as one of synchronizing two
dynamical systems, one representing "truth" and the other representing
"model", with a unidirectional flow of information between the two.
Synchronization of truth and model defines
a general view of data assimilation, as machine
perception, that is reminiscent of the Jung-Pauli notion of
synchronicity between matter and mind.
The dynamical systems paradigm of the synchronization of a pair of
loosely coupled chaotic systems is expected to be useful
because quasi-2D geophysical fluid models have
been shown to synchronize when only medium-scale modes are coupled. The
synchronization approach is equivalent to standard approaches based
on least-squares optimization, including Kalman filtering, except in highly
non-linear regions of state space where observational noise links regimes
with qualitatively different dynamics. The synchronization approach is
used to calculate covariance
inflation factors from parameters describing the bimodality of a
one-dimensional system. The factors agree in overall magnitude with those
used in operational practice on an ad hoc basis. The calculation
is robust against the introduction
of stochastic model error arising from unresolved scales. |
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