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Communication Dans Un Congrès Année : 2012

DATA ASSIMILATION ON A FLOOD WAVE PROPAGATION MODEL : EMULATION OF A KALMAN FILTER ALGORITHM

Résumé

This study describes the assimilation of synthetically-generated river water level observations in a flood wave propagation model. For this approach to be applied in the framework of real-time flood forecasting, the cost of the data assimilation procedure, mostly related to the estimation of the background error covariance matrix, should be bound. An Ensemble Kalman Filter (EnKF) algorithm is applied, with a steady observation network, to demonstrate how the assimilation modifies the background correlation function at the observation point. It is shown that an initially Gaussian correlation function turns into an anisotropic function at the observation point, with a shorter correlation length-scale downstream of the observation point than upstream, and that the variance of the error in the water level state is significantly reduced downstream of the observation point. The covariance matrix resulting from the EnKF is then used as an invariant background error covariance matrix for a series of successive Best Linear Unbiased Estimation (BLUE) algorithms which emulate an EnKF at a lower cost. This study shows how the background error covariance matrix can be computed off-line, with an advanced algorithm, and then used with a cheaper algorithm for real-time application.
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Dates et versions

hal-01335907 , version 1 (22-06-2016)

Identifiants

  • HAL Id : hal-01335907 , version 1

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Sébastien Barthélémy, Sophie Ricci, Olivier Pannekoucke, Olivier Thual, Pierre-Olivier Malaterre. DATA ASSIMILATION ON A FLOOD WAVE PROPAGATION MODEL : EMULATION OF A KALMAN FILTER ALGORITHM. Simhydro 2012, Sep 2012, Nice, France. ⟨hal-01335907⟩
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