A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

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

A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models

Type

Conference Paper

Authors

El Gharamti, Mohamad

Ait-El-Fquih, Boujemaa
Hoteit, Ibrahim

KAUST Department

Applied Mathematics and Computational Science Program
Earth Fluid Modeling and Prediction Group
Earth Science and Engineering Program
Physical Science and Engineering (PSE) Division

Online Publication Date

2015-11-27

Print Publication Date

2015

Date

2015-11-27

Abstract

The ensemble Kalman filter (EnKF) recursively integrates field data into simulation models to obtain a better characterization of the model’s state and parameters. These are generally estimated following a state-parameters joint augmentation strategy. In this study, we introduce a new smoothing-based joint EnKF scheme, in which we introduce a one-step-ahead smoothing of the state before updating the parameters. Numerical experiments are performed with a two-dimensional synthetic subsurface contaminant transport model. The improved performance of the proposed joint EnKF scheme compared to the standard joint EnKF compensates for the modest increase in the computational cost.

Citation

Gharamti ME, Ait-El-Fquih B, Hoteit I (2015) A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models. Lecture Notes in Computer Science: 207–214. Available: http://dx.doi.org/10.1007/978-3-319-25138-7_19.