Articles | Volume 19, issue 12
https://doi.org/10.5194/hess-19-4811-2015
© Author(s) 2015. 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-19-4811-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
16 Dec 2015
Research article |
| 16 Dec 2015
Comparing the ensemble and extended Kalman filters for in situ soil moisture assimilation with contrasting conditions
D. Fairbairn
CNRM-GAME, UMR3589 – Météo-France, CNRS, Toulouse, France
A. L. Barbu
CNRM-GAME, UMR3589 – Météo-France, CNRS, Toulouse, France
J.-F. Mahfouf
CNRM-GAME, UMR3589 – Météo-France, CNRS, Toulouse, France
J.-C. Calvet
CORRESPONDING AUTHOR
CNRM-GAME, UMR3589 – Météo-France, CNRS, Toulouse, France
E. Gelati
CNRM-GAME, UMR3589 – Météo-France, CNRS, Toulouse, France
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Cited
16 citations as recorded by crossref.
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- Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen M. Babel et al. 10.1080/1573062X.2020.1828938
- Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model E. Gelati et al. 10.5194/hess-22-2091-2018
- Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. 10.1016/j.advwatres.2020.103813
- Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses C. Draper & R. Reichle 10.1175/MWR-D-18-0393.1
- Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak 10.1029/2021MS002533
- Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability J. Zhou et al. 10.1080/02626667.2019.1662024
- Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. 10.3390/rs11050520
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
- The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France D. Fairbairn et al. 10.5194/hess-21-2015-2017
- Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review R. Khandan et al. 10.3390/rs14030770
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- Improved streamflow simulations by coupling soil moisture analytical relationship in EnKF based hydrological data assimilation framework A. Patil & R. Ramsankaran 10.1016/j.advwatres.2018.08.010
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- Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces C. Albergel et al. 10.5194/hess-24-4291-2020
16 citations as recorded by crossref.
- Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area C. Albergel et al. 10.5194/gmd-10-3889-2017
- Data assimilation for flow forecasting in urban drainage systems by updating a hydrodynamic model of Damhusåen Catchment, Copenhagen M. Babel et al. 10.1080/1573062X.2020.1828938
- Hydrological assessment of atmospheric forcing uncertainty in the Euro-Mediterranean area using a land surface model E. Gelati et al. 10.5194/hess-22-2091-2018
- Improving parameter and state estimation of a hydrological model with the ensemble square root filter N. Li et al. 10.1016/j.advwatres.2020.103813
- Assimilation of Satellite Soil Moisture for Improved Atmospheric Reanalyses C. Draper & R. Reichle 10.1175/MWR-D-18-0393.1
- Implementation of an Adaptive Bias‐Aware Extended Kalman Filter for Sea‐Ice Data Assimilation in the HARMONIE‐AROME Numerical Weather Prediction System Y. Batrak 10.1029/2021MS002533
- Regional assimilation of in situ observed soil moisture into the VIC model considering spatial variability J. Zhou et al. 10.1080/02626667.2019.1662024
- Monitoring and Forecasting the Impact of the 2018 Summer Heatwave on Vegetation C. Albergel et al. 10.3390/rs11050520
- An ensemble square root filter for the joint assimilation of surface soil moisture and leaf area index within the Land Data Assimilation System LDAS-Monde: application over the Euro-Mediterranean region B. Bonan et al. 10.5194/hess-24-325-2020
- The International Soil Moisture Network: serving Earth system science for over a decade W. Dorigo et al. 10.5194/hess-25-5749-2021
- The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France D. Fairbairn et al. 10.5194/hess-21-2015-2017
- Assimilation of Satellite-Derived Soil Moisture and Brightness Temperature in Land Surface Models: A Review R. Khandan et al. 10.3390/rs14030770
- An Evaluation of the EnKF vs. EnOI and the Assimilation of SMAP, SMOS and ESA CCI Soil Moisture Data over the Contiguous US J. Blyverket et al. 10.3390/rs11050478
- Improved streamflow simulations by coupling soil moisture analytical relationship in EnKF based hydrological data assimilation framework A. Patil & R. Ramsankaran 10.1016/j.advwatres.2018.08.010
- An offline framework for high-dimensional ensemble Kalman filters to reduce the time to solution Y. Zheng et al. 10.5194/gmd-13-3607-2020
- Data assimilation for continuous global assessment of severe conditions over terrestrial surfaces C. Albergel et al. 10.5194/hess-24-4291-2020
Saved (final revised paper)
Latest update: 20 Apr 2024
Short summary
The ensemble Kalman filter (EnKF) and simplified extended Kalman filter (SEKF) root-zone soil moisture analyses are compared when assimilating in situ surface observations. In the synthetic experiments, the EnKF performs best because it can stochastically capture the errors in the precipitation. The two methods perform similarly in the real experiments. During the summer period, both methods perform poorly as a result of nonlinearities in the land surface model.
The ensemble Kalman filter (EnKF) and simplified extended Kalman filter (SEKF) root-zone soil...
