Articles | Volume 20, issue 8
https://doi.org/10.5194/hess-20-3289-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-20-3289-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
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
Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
Mohamad El Gharamti
Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
Mohn-Sverdrup Center for Global Ocean Studies and Operational Oceanography, Nansen Environmental and Remote Sensing Center (NERSC), 5006 Bergen, Norway
Ibrahim Hoteit
CORRESPONDING AUTHOR
Department of Earth Sciences and Engineering, King Abdullah University of Science and Technology (KAUST), 23955-6900 Thuwal, Kingdom of Saudi Arabia
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Cited
27 citations as recorded by crossref.
- Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation M. Ziliani et al. 10.1016/j.jhydrol.2019.123924
- A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraint M. Khaki et al. 10.1016/j.jhydrol.2017.10.032
- A variational Bayesian approach for ensemble filtering of stochastically parametrized systems B. Ait‐El‐Fquih et al. 10.1002/qj.4481
- Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation J. Tian et al. 10.1007/s11430-022-1180-8
- New graphical models for sequential data and the improved state estimations by data-conditioned driving noises W. Lee 10.1186/s13634-024-01145-z
- A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
- Monitoring water storage decline over the Middle East M. Khaki & I. Hoteit 10.1016/j.jhydrol.2021.127166
- Feature-Oriented Joint Time-Lapse Seismic and Electromagnetic History Matching Using Ensemble Methods Y. Zhang & I. Hoteit 10.2118/203847-PA
- Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter R. Ghostine et al. 10.3390/math9192427
- Ensemble Kalman filter inference of spatially-varying Manning’s n coefficients in the coastal ocean A. Siripatana et al. 10.1016/j.jhydrol.2018.05.021
- Bias Correction of Hydrologic Projections Strongly Impacts Inferred Climate Vulnerabilities in Institutionally Complex Water Systems K. Malek et al. 10.1061/(ASCE)WR.1943-5452.0001493
- Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment T. Singh et al. 10.3389/fmars.2022.775394
- Ensemble Kalman Filtering with One-Step-Ahead Smoothing N. Raboudi et al. 10.1175/MWR-D-17-0175.1
- Parametric Bayesian estimation of point-like pollution sources of groundwater layers B. Ait-El-Fquih et al. 10.1016/j.sigpro.2019.107339
- Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator A. Arnold et al. 10.1115/1.4035918
- Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. 10.1038/s41598-020-75710-5
- An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit 10.1175/MWR-D-16-0485.1
- Online estimation of colored observation‐noise parameters within an ensemble Kalman filtering framework N. Raboudi et al. 10.1002/qj.4484
- An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter R. Ghostine et al. 10.3390/math9060636
- Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic M. Gharamti et al. 10.1016/j.jmarsys.2016.12.003
- Calibrating land hydrological models and enhancing their forecasting skills using an ensemble Kalman filter with one-step-ahead smoothing M. Khaki et al. 10.1016/j.jhydrol.2020.124708
- A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
- An approach to periodic, time-varying parameter estimation using nonlinear filtering A. Arnold & A. Lloyd 10.1088/1361-6420/aad3e0
- Parameter Set Reduction and Ensemble Kalman Filtering for Engine Model Calibration R. Salehi & A. Stefanopoulou 10.1115/1.4045090
- Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models? Y. Sawada 10.5194/hess-24-3881-2020
- Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter N. Raboudi et al. 10.1175/MWR-D-18-0410.1
- 基于扩展卡尔曼滤波和自动微分技术对陆面数据同化系统参数的快速估计及其影响 佳. 田 et al. 10.1360/SSTe-2022-0372
27 citations as recorded by crossref.
- Enhanced flood forecasting through ensemble data assimilation and joint state-parameter estimation M. Ziliani et al. 10.1016/j.jhydrol.2019.123924
- A two-update ensemble Kalman filter for land hydrological data assimilation with an uncertain constraint M. Khaki et al. 10.1016/j.jhydrol.2017.10.032
- A variational Bayesian approach for ensemble filtering of stochastically parametrized systems B. Ait‐El‐Fquih et al. 10.1002/qj.4481
- Quick estimation of parameters for the land surface data assimilation system and its influence based on the extended Kalman filter and automatic differentiation J. Tian et al. 10.1007/s11430-022-1180-8
- New graphical models for sequential data and the improved state estimations by data-conditioned driving noises W. Lee 10.1186/s13634-024-01145-z
- A particle‐filter based adaptive inflation scheme for the ensemble Kalman filter B. Ait‐El‐Fquih & I. Hoteit 10.1002/qj.3716
- Monitoring water storage decline over the Middle East M. Khaki & I. Hoteit 10.1016/j.jhydrol.2021.127166
- Feature-Oriented Joint Time-Lapse Seismic and Electromagnetic History Matching Using Ensemble Methods Y. Zhang & I. Hoteit 10.2118/203847-PA
- Mathematical Modeling of Immune Responses against SARS-CoV-2 Using an Ensemble Kalman Filter R. Ghostine et al. 10.3390/math9192427
- Ensemble Kalman filter inference of spatially-varying Manning’s n coefficients in the coastal ocean A. Siripatana et al. 10.1016/j.jhydrol.2018.05.021
- Bias Correction of Hydrologic Projections Strongly Impacts Inferred Climate Vulnerabilities in Institutionally Complex Water Systems K. Malek et al. 10.1061/(ASCE)WR.1943-5452.0001493
- Estimation of Ocean Biogeochemical Parameters in an Earth System Model Using the Dual One Step Ahead Smoother: A Twin Experiment T. Singh et al. 10.3389/fmars.2022.775394
- Ensemble Kalman Filtering with One-Step-Ahead Smoothing N. Raboudi et al. 10.1175/MWR-D-17-0175.1
- Parametric Bayesian estimation of point-like pollution sources of groundwater layers B. Ait-El-Fquih et al. 10.1016/j.sigpro.2019.107339
- Uncertainty Quantification in a Patient-Specific One-Dimensional Arterial Network Model: EnKF-Based Inflow Estimator A. Arnold et al. 10.1115/1.4035918
- Multi-mission satellite remote sensing data for improving land hydrological models via data assimilation M. Khaki et al. 10.1038/s41598-020-75710-5
- An Efficient State–Parameter Filtering Scheme Combining Ensemble Kalman and Particle Filters B. Ait-El-Fquih & I. Hoteit 10.1175/MWR-D-16-0485.1
- Online estimation of colored observation‐noise parameters within an ensemble Kalman filtering framework N. Raboudi et al. 10.1002/qj.4484
- An Extended SEIR Model with Vaccination for Forecasting the COVID-19 Pandemic in Saudi Arabia Using an Ensemble Kalman Filter R. Ghostine et al. 10.3390/math9060636
- Online tuning of ocean biogeochemical model parameters using ensemble estimation techniques: Application to a one-dimensional model in the North Atlantic M. Gharamti et al. 10.1016/j.jmarsys.2016.12.003
- Calibrating land hydrological models and enhancing their forecasting skills using an ensemble Kalman filter with one-step-ahead smoothing M. Khaki et al. 10.1016/j.jhydrol.2020.124708
- A fast, single-iteration ensemble Kalman smoother for sequential data assimilation C. Grudzien & M. Bocquet 10.5194/gmd-15-7641-2022
- An approach to periodic, time-varying parameter estimation using nonlinear filtering A. Arnold & A. Lloyd 10.1088/1361-6420/aad3e0
- Parameter Set Reduction and Ensemble Kalman Filtering for Engine Model Calibration R. Salehi & A. Stefanopoulou 10.1115/1.4045090
- Do surface lateral flows matter for data assimilation of soil moisture observations into hyperresolution land models? Y. Sawada 10.5194/hess-24-3881-2020
- Combining Hybrid and One-Step-Ahead Smoothing for Efficient Short-Range Storm Surge Forecasting with an Ensemble Kalman Filter N. Raboudi et al. 10.1175/MWR-D-18-0410.1
- 基于扩展卡尔曼滤波和自动微分技术对陆面数据同化系统参数的快速估计及其影响 佳. 田 et al. 10.1360/SSTe-2022-0372
Saved (preprint)
Latest update: 21 Nov 2024
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.
We derive a new dual ensemble Kalman filter (EnKF) for state-parameter estimation. The...
