Articles | Volume 15, issue 8
https://doi.org/10.5194/hess-15-2437-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-15-2437-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
03 Aug 2011
Research article |
| 03 Aug 2011
Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments
S. Nie
National Climate Center, China Meteorological Administration, Beijing, China
J. Zhu
Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing China
Y. Luo
National Climate Center, China Meteorological Administration, Beijing, China
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- Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter H. Bi et al. 10.1007/s11430-013-4742-y
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- An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China J. Huang & J. Gao 10.1016/j.ecolmodel.2017.04.019
26 citations as recorded by crossref.
- Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation B. Naz et al. 10.5194/hess-23-277-2019
- A partitioned update scheme for state‐parameter estimation of distributed hydrologic models based on the ensemble Kalman filter X. Xie & D. Zhang 10.1002/2012WR012853
- Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities Y. Liu et al. 10.5194/hess-16-3863-2012
- Spatial horizontal correlation characteristics in the land data assimilation of soil moisture X. Han et al. 10.5194/hess-16-1349-2012
- Simultaneous estimation of soil moisture and hydraulic parameters using residual resampling particle filter H. Bi et al. 10.1007/s11430-013-4742-y
- On the difficulty to optimally implement the Ensemble Kalman filter: An experiment based on many hydrological models and catchments A. Thiboult & F. Anctil 10.1016/j.jhydrol.2015.09.036
- Parameter-state ensemble thinning for short-term hydrological prediction B. Davison et al. 10.5194/hess-23-741-2019
- Global Soil Moisture Estimation by Assimilating AMSR-E Brightness Temperatures in a Coupled CLM4–RTM–DART System L. Zhao et al. 10.1175/JHM-D-15-0218.1
- An integrated error parameter estimation and lag-aware data assimilation scheme for real-time flood forecasting Y. Li et al. 10.1016/j.jhydrol.2014.08.009
- Evolution of ensemble data assimilation for uncertainty quantification using the particle filter‐Markov chain Monte Carlo method H. Moradkhani et al. 10.1029/2012WR012144
- Assimilation of stream discharge for flood forecasting: Updating a semidistributed model with an integrated data assimilation scheme Y. Li et al. 10.1002/2014WR016667
- Correction of systematic model forcing bias of CLM using assimilation of cosmic-ray Neutrons and land surface temperature: a study in the Heihe Catchment, China X. Han et al. 10.5194/hess-19-615-2015
- Sinusoidal acceleration harmonic estimation using the extended Kalman filter for an electro-hydraulic servo shaking table J. Yao et al. 10.1177/1077546313499392
- Sequential streamflow assimilation for short-term hydrological ensemble forecasting M. Abaza et al. 10.1016/j.jhydrol.2014.08.038
- Multiscale Assimilation of Sentinel and Landsat Data for Soil Moisture and Leaf Area Index Predictions Using an Ensemble-Kalman-Filter-Based Assimilation Approach in a Heterogeneous Ecosystem N. Montaldo et al. 10.3390/rs14143458
- Soil moisture and soil properties estimation in the Community Land Model with synthetic brightness temperature observations X. Han et al. 10.1002/2013WR014586
- Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance J. Rasmussen et al. 10.5194/hess-19-2999-2015
- Identification of hydrological model parameter variation using ensemble Kalman filter C. Deng et al. 10.5194/hess-20-4949-2016
- Comparison of Sequential and Variational Streamflow Assimilation Techniques for Short-Term Hydrological Forecasting M. Abaza et al. 10.1061/(ASCE)HE.1943-5584.0001013
- Performance of AMSR_E soil moisture data assimilation in CLM4.5 model for monitoring hydrologic fluxes at global scale D. Liu & A. Mishra 10.1016/j.jhydrol.2017.01.036
- Paleoclimate data assimilation: Its motivation, progress and prospects M. Fang & X. Li 10.1007/s11430-015-5432-6
- 古气候数据同化<bold>: </bold>缘起<bold>、</bold>进展与展望 苗. 方 & 新. 李 10.1360/N072015-00432
- Assessing the Potential of Upcoming Satellite Altimeter Missions in Operational Flood Forecasting Systems A. Falck et al. 10.3390/rs13214459
- Assimilation of NDVI data in a land surface – Vegetation model for leaf area index predictions in a tree-grass ecosystem N. Montaldo et al. 10.1080/17538947.2023.2259226
- A comparison of methods for a priori bias correction in soil moisture data assimilation S. Kumar et al. 10.1029/2010WR010261
- An improved Ensemble Kalman Filter for optimizing parameters in a coupled phosphorus model for lowland polders in Lake Taihu Basin, China J. Huang & J. Gao 10.1016/j.ecolmodel.2017.04.019
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