A one-step-ahead smoothing-based joint ensemble kalman filter for state-parameter estimation of hydrological models

Mohamad E. Gharamti, Boujemaa Ait-El-Fquih, Ibrahim Hoteit

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationDynamic Data-Driven Environmental Systems Science - 1st International Conference, DyDESS 2014, Revised Selected Papers
EditorsAdrian Sandu, Sai Ravela
PublisherSpringer Verlag
Pages207-214
Number of pages8
ISBN (Print)9783319251370
DOIs
StatePublished - 2015
Event1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014 - Cambridge, United States
Duration: Nov 5 2014Nov 7 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8964
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014
Country/TerritoryUnited States
CityCambridge
Period11/5/1411/7/14

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