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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 20, issue 6
Nonlin. Processes Geophys., 20, 955–964, 2013
https://doi.org/10.5194/npg-20-955-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: Ensemble methods in geophysical sciences

Nonlin. Processes Geophys., 20, 955–964, 2013
https://doi.org/10.5194/npg-20-955-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 08 Nov 2013

Research article | 08 Nov 2013

Using ensemble data assimilation to forecast hydrological flumes

I. Amour, Z. Mussa, A. Bibov, and T. Kauranne I. Amour et al.
  • Lappeenranta University of Technology, Lappeenranta, Finland

Abstract. 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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