Hydrology and Earth System Sciences
Hydrology and Earth System Sciences
HESS
Articles | Volume 20, issue 8
Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3289-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-3289-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
Articles | Volume 20, issue 8
Research article
 | 
12 Aug 2016
Research article |  | 12 Aug 2016

A Bayesian consistent dual ensemble Kalman filter for state-parameter estimation in subsurface hydrology

Boujemaa Ait-El-Fquih, Mohamad El Gharamti, and Ibrahim Hoteit

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Short summary
We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The derivation is based on the one-step-ahead smoothing formulation, and unlike the standard dual EnKF, it is consistent with the Bayesian formulation of the state-parameter estimation problem and uses the observations in both state smoothing and forecast. This is shown to enhance the performance and robustness of the dual EnKF in experiments conducted with a two-dimensional synthetic groundwater aquifer model.
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