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Hydrology and Earth System Sciences An interactive open-access journal of the European Geosciences Union
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https://doi.org/10.5194/hessd-10-6963-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hessd-10-6963-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.

  03 Jun 2013

03 Jun 2013

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This preprint has been withdrawn by the authors.

Emulation of an ensemble Kalman filter algorithm on a flood wave propagation model

S. Barthélémy1, S. Ricci1, O. Pannekoucke1,2, O. Thual1,3, and P. O. Malaterre4 S. Barthélémy et al.
  • 1CERFACS – URA1875, Toulouse, France
  • 2CNRM-GAME – UMR3589, Toulouse, France
  • 3INPT, CNRS, IMFT, Toulouse, France
  • 4UMR G-EAU, Irstea, Montpellier, France

Abstract. This study describes the emulation of an Ensemble Kalman Filter (EnKF) algorithm on a 1-D flood wave propagation model. This model is forced at the upstream boundary with a random variable with gaussian statistics and a correlation function in time with gaussian shape. This allows for, in the case without assimilation, the analytical study of the covariance functions of the propagated signal anomaly. This study is validated numerically with an ensemble method. In the case with assimilation with one observation point, where synthetical observations are generated by adding an error to a true state, the dynamic of the background error covariance functions is not straightforward and a numerical approach using an EnKF algorithm is prefered. First, those numerical experiments show that both background error variance and correlation length scale are reduced at the observation point. This reduction of variance and correlation length scale is propagated downstream by the dynamics of the model. Then, it is shown that the application of a Best Linear Unbiased Estimator (BLUE) algorithm using the background error covariance matrix converged from the EnKF algorithm, provides the same results as the EnKF but with a cheaper computational cost, thus allowing for the use of data assimilation in the context of real time flood forecasting. Moreover it was demonstrated that the reduction of background error correlation length scale and variance at the observation point depends on the error observation statistics. This feature is quantified by abacus built from linear regressions over a limited set of EnKF experiments. These abacus that describe the background error variance and the correlation length scale in the neighboring of the observation point combined with analytical expressions that describe the background error variance and the correlation length scale away from the observation point provide parametrized models for the variance and the correlation length scale. Using this parametrized variance and correlation length scale with a diffusion operator makes it possible to model the converged background error covariance matrix from the EnKF without actually integrating the EnKF algorithm. This method was finally applied to a case with two different observation point with different error statistics. It was shown that the results of this emulated EnKF (EEnKF) in terms of background error variance, correlation length scale and analyzed water level is close to those of the EnKF but with a significantly reduced computational cost.

This preprint has been withdrawn.

S. Barthélémy et al.

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S. Barthélémy et al.

S. Barthélémy et al.

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