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

Viewed

Total article views: 4,335 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,252 1,809 274 4,335 152 185
  • HTML: 2,252
  • PDF: 1,809
  • XML: 274
  • Total: 4,335
  • BibTeX: 152
  • EndNote: 185
Views and downloads (calculated since 01 Feb 2016)
Cumulative views and downloads (calculated since 01 Feb 2016)

Cited

Saved (final revised paper)

Latest update: 01 May 2026
Download
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.
Share