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| Titel |
Detecting nonlinearity in run-up on a natural beach |
| VerfasserIn |
K. R. Bryan, G. Coco |
| 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 ; 14, no. 4 ; Nr. 14, no. 4 (2007-07-12), S.385-393 |
| Datensatznummer |
250012238
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| Publikation (Nr.) |
 copernicus.org/npg-14-385-2007.pdf |
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| Zusammenfassung |
| Natural geophysical timeseries bear the signature of a number of complex,
possibly inseparable, and generally unknown combination of linear, stable
non-linear and chaotic processes. Quantifying the relative contribution of,
in particular, the non-linear components will allow improved modelling and
prediction of natural systems, or at least define some limitations on
predictability. However, difficulties arise; for example, in cases where the series are
naturally cyclic (e.g. water waves), it is most unclear how this
cyclic behaviour impacts on the techniques commonly used to detect the
nonlinear behaviour in other fields. Here a non-linear autoregressive
forecasting technique which has had success in demonstrating nonlinearity in
non-cyclical geophysical timeseries, is applied to a timeseries generated by
videoing the waterline on a natural beach (run-up), which has some irregular
oscillatory behaviour that is in part induced by the incoming wave field. In
such cases, the deterministic shape of each run-up cycle has a strong
influence on forecasting results, causing questionable results at small
(within a cycle) prediction distances. However, the technique can clearly
differentiate between random surrogate series and natural timeseries at
larger prediction distances (greater than one cycle). Therefore it was
possible to clearly identify nonlinearity in the relationship between
observed run-up cycles in that a local autoregressive model was more adept
at predicting run-up cycles than a global one. Results suggest that despite
forcing from waves impacting on the beach, each run-up cycle evolves
somewhat independently, depending on a non-linear interaction with previous
run-up cycles. More generally, a key outcome of the study is that
oscillatory data provide a similar challenge to differentiating chaotic
signals from correlated noise in that the deterministic shape causes an
additional source of autocorrelation which in turn influences the
predictability at small forecasting distances. |
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