Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3289-2016
https://doi.org/10.5194/hess-20-3289-2016
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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Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
ED: Reconsider after major revisions (12 Apr 2016) by Mauro Giudici
AR by Boujemaa Ait-El-Fquih on behalf of the Authors (23 Apr 2016)  Author's response   Manuscript 
ED: Referee Nomination & Report Request started (25 Apr 2016) by Mauro Giudici
RR by Anonymous Referee #1 (12 May 2016)
RR by Anonymous Referee #3 (03 Jun 2016)
ED: Publish subject to minor revisions (Editor review) (15 Jun 2016) by Mauro Giudici
AR by Boujemaa Ait-El-Fquih on behalf of the Authors (29 Jun 2016)
ED: Publish subject to technical corrections (10 Jul 2016) by Mauro Giudici
AR by Boujemaa Ait-El-Fquih on behalf of the Authors (18 Jul 2016)  Author's response   Manuscript 
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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.