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

TitleA One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models
Publication TypeJournal Article
Year of Publication2015
AuthorsGharamti, ME, Ait-El-Fquih, B, Hoteit, I
JournalLecture Notes in Computer Science
Volume8964
PublisherSpringer Verlag
ISSN0302-9743
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.

DOI10.1007/978-3-319-25138-7_19
Refereed DesignationRefereed
Author Address

NERSC