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| Titel |
Pattern dynamics, pattern hierarchies, and forecasting in complex multi-scale earth systems |
| VerfasserIn |
J. B. Rundle, D. L. Turcotte, P. B. Rundle, R. Shcherbakov, G. Yakovlev, A. Donnellan, W. Klein |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1027-5606
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| Digitales Dokument |
URL |
| Erschienen |
In: Hydrology and Earth System Sciences ; 10, no. 6 ; Nr. 10, no. 6 (2006-10-30), S.789-796 |
| Datensatznummer |
250008282
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| Publikation (Nr.) |
 copernicus.org/hess-10-789-2006.pdf |
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| Zusammenfassung |
| Catastrophic disasters afflicting human society are often triggered
by tsunamis, earthquakes, widespread flooding, and weather and
climate events. As human populations increasingly move into
geographic areas affected by these earth system hazards, forecasting
the onset of these large and damaging events has become increasingly
urgent. In this paper we consider the fundamental problem of
forecasting in complex multi-scale earth systems when the basic
dynamical variables are either unobservable or incompletely
observed. In such cases, the forecaster must rely on incompletely
determined, but "tunable" models to interpret observable
space-time patterns of events. The sequence of observable patterns
constitute an apparent pattern dynamics, which is related to the
underlying but hidden dynamics by a complex dimensional reduction
process. As an example, we examine the problem of earthquakes, which
must utilize current and past observations of observables such as
seismicity and surface strain to produce forecasts of future
activity. We show that numerical simulations of earthquake fault
systems are needed in order to relate the fundamentally unobservable
nonlinear dynamics to the readily observable pattern dynamics. We
also show that the space-time patterns produced by the simulations
lead to a scale-invariant hierarchy of patterns, similar to other
nonlinear systems. We point out that a similar program of
simulations has been very successful in weather forecasting, in
which current and past observations of weather patterns are
routinely extrapolated forward in time via numerical simulations in
order to forecast future weather patterns. |
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