A One-Step-Ahead Smoothing-Based Joint Ensemble Kalman Filter for State-Parameter Estimation of Hydrological Models
KAUST DepartmentEarth Science and Engineering Program
Applied Mathematics and Computational Science Program
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AbstractThe 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.
CitationGharamti 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.
PublisherSpringer Science + Business Media
Conference/Event name1st International Conference on Dynamic Data-Driven Environmental Systems Science, DyDESS 2014