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| Titel |
Using ensemble data assimilation to forecast hydrological flumes |
| VerfasserIn |
I. Amour, Z. Mussa, A. Bibov, T. Kauranne |
| 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 (2013-11-08), S.955-964 |
| Datensatznummer |
250086069
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| Publikation (Nr.) |
 copernicus.org/npg-20-955-2013.pdf |
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| Zusammenfassung |
| Data assimilation, commonly used in weather forecasting, means combining a
mathematical forecast of a target dynamical system with simultaneous
measurements from that system in an optimal fashion. We demonstrate the
benefits obtainable from data assimilation with a dam break flume simulation
in which a shallow-water equation model is complemented with wave meter
measurements. Data assimilation is conducted with a Variational Ensemble
Kalman Filter (VEnKF) algorithm. The resulting dynamical analysis of the
flume displays turbulent behavior, features prominent hydraulic jumps and
avoids many numerical artifacts present in a pure simulation. |
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