Articles | Volume 17, issue 9
https://doi.org/10.5194/hess-17-3499-2013
© Author(s) 2013. 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-17-3499-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
13 Sep 2013
Research article |
| 13 Sep 2013
Simultaneous estimation of model state variables and observation and forecast biases using a two-stage hybrid Kalman filter
V. R. N. Pauwels
Department of Civil Engineering, Monash University, Clayton, Victoria, Australia
G. J. M. De Lannoy
NASA Goddard Space Flight Center, Greenbelt, MD, USA
H.-J. Hendricks Franssen
Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Jülich, Germany
H. Vereecken
Forschungszentrum Jülich GmbH, Agrosphere (IBG-3), Jülich, Germany
Viewed
Total article views: 5,156 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,756 | 3,208 | 192 | 5,156 | 151 | 143 |
- HTML: 1,756
- PDF: 3,208
- XML: 192
- Total: 5,156
- BibTeX: 151
- EndNote: 143
Total article views: 3,676 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 13 Sep 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 1,341 | 2,171 | 164 | 3,676 | 137 | 134 |
- HTML: 1,341
- PDF: 2,171
- XML: 164
- Total: 3,676
- BibTeX: 137
- EndNote: 134
Total article views: 1,480 (including HTML, PDF, and XML)
Cumulative views and downloads
(calculated since 24 Apr 2013)
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 415 | 1,037 | 28 | 1,480 | 14 | 9 |
- HTML: 415
- PDF: 1,037
- XML: 28
- Total: 1,480
- BibTeX: 14
- EndNote: 9
Cited
31 citations as recorded by crossref.
- Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System D. Fairbairn et al. 10.3390/rs16030493
- Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. 10.1016/j.advwatres.2017.12.006
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales P. Zhu et al. 10.1016/j.jhydrol.2017.10.078
- Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models V. Pauwels et al. 10.3389/frwa.2020.00004
- Operational aspects of asynchronous filtering for flood forecasting O. Rakovec et al. 10.5194/hess-19-2911-2015
- Data assimilation in integrated hydrological modelling in the presence of observation bias J. Rasmussen et al. 10.5194/hess-20-2103-2016
- Regularized variational data assimilation for bias treatment using the Wasserstein metric S. Tamang et al. 10.1002/qj.3794
- Observation and Model Bias Estimation in the Presence of Either or Both Sources of Error R. Lorente-Plazas & J. Hacker 10.1175/MWR-D-16-0273.1
- A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System C. Draper et al. 10.1175/JHM-D-14-0087.1
- Comparing Assimilation of Synthetic Soil Moisture Versus C‐Band Backscatter for Hyper‐Resolution Land Surface Modeling L. Sun et al. 10.1029/2020WR028921
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. 10.3389/frwa.2022.981745
- SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia H. Lievens et al. 10.1016/j.rse.2015.06.025
- Informing hydrogeological models with remotely sensed evapotranspiration S. Gelsinari et al. 10.3389/frwa.2022.932641
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Bias-aware data assimilation in integrated hydrological modelling M. Ridler et al. 10.2166/nh.2017.117
- Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation G. De Lannoy & R. Reichle 10.1175/JHM-D-15-0037.1
- Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. 10.3390/rs8060456
- Satellite-supported flood forecasting in river networks: A real case study J. García-Pintado et al. 10.1016/j.jhydrol.2015.01.084
- Data assimilation of surface displacements to improve geomechanical parameters of gas storage reservoirs C. Zoccarato et al. 10.1002/2015JB012090
- Soil hydrology: Recent methodological advances, challenges, and perspectives H. Vereecken et al. 10.1002/2014WR016852
- Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation J. Dong et al. 10.1016/j.rse.2020.111756
- Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling S. Gelsinari et al. 10.5194/hess-25-2261-2021
- Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles H. Flynn & G. Larsen 10.1016/j.fusengdes.2022.113037
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- Error covariance calculation for forecast bias estimation in hydrologic data assimilation V. Pauwels & G. De Lannoy 10.1016/j.advwatres.2015.05.013
- Feasibility of Improving Groundwater Modeling by Assimilating Evapotranspiration Rates S. Gelsinari et al. 10.1029/2019WR025983
- Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
- On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models S. Noh et al. 10.1016/j.jhydrol.2014.07.049
- Challenges, Opportunities, and Pitfalls for Global Coupled Hydrologic‐Hydraulic Modeling of Floods S. Grimaldi et al. 10.1029/2018WR024289
30 citations as recorded by crossref.
- Evaluation of an Adaptive Soil Moisture Bias Correction Approach in the ECMWF Land Data Assimilation System D. Fairbairn et al. 10.3390/rs16030493
- Insights on the impact of systematic model errors on data assimilation performance in changing catchments S. Pathiraja et al. 10.1016/j.advwatres.2017.12.006
- Recursive Bayesian Estimation of Conceptual Rainfall‐Runoff Model Errors in Real‐Time Prediction of Streamflow M. Tajiki et al. 10.1029/2019WR025237
- Data assimilation of soil water flow via ensemble Kalman filter: Infusing soil moisture data at different scales P. Zhu et al. 10.1016/j.jhydrol.2017.10.078
- Evaluation of State and Bias Estimates for Assimilation of SMOS Retrievals Into Conceptual Rainfall-Runoff Models V. Pauwels et al. 10.3389/frwa.2020.00004
- Operational aspects of asynchronous filtering for flood forecasting O. Rakovec et al. 10.5194/hess-19-2911-2015
- Data assimilation in integrated hydrological modelling in the presence of observation bias J. Rasmussen et al. 10.5194/hess-20-2103-2016
- Regularized variational data assimilation for bias treatment using the Wasserstein metric S. Tamang et al. 10.1002/qj.3794
- Observation and Model Bias Estimation in the Presence of Either or Both Sources of Error R. Lorente-Plazas & J. Hacker 10.1175/MWR-D-16-0273.1
- A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System C. Draper et al. 10.1175/JHM-D-14-0087.1
- Comparing Assimilation of Synthetic Soil Moisture Versus C‐Band Backscatter for Hyper‐Resolution Land Surface Modeling L. Sun et al. 10.1029/2020WR028921
- Generating Ensemble Streamflow Forecasts: A Review of Methods and Approaches Over the Past 40 Years M. Troin et al. 10.1029/2020WR028392
- Perspective on satellite-based land data assimilation to estimate water cycle components in an era of advanced data availability and model sophistication G. De Lannoy et al. 10.3389/frwa.2022.981745
- SMOS soil moisture assimilation for improved hydrologic simulation in the Murray Darling Basin, Australia H. Lievens et al. 10.1016/j.rse.2015.06.025
- Informing hydrogeological models with remotely sensed evapotranspiration S. Gelsinari et al. 10.3389/frwa.2022.932641
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Bias-aware data assimilation in integrated hydrological modelling M. Ridler et al. 10.2166/nh.2017.117
- Global Assimilation of Multiangle and Multipolarization SMOS Brightness Temperature Observations into the GEOS-5 Catchment Land Surface Model for Soil Moisture Estimation G. De Lannoy & R. Reichle 10.1175/JHM-D-15-0037.1
- Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review Y. Li et al. 10.3390/rs8060456
- Satellite-supported flood forecasting in river networks: A real case study J. García-Pintado et al. 10.1016/j.jhydrol.2015.01.084
- Data assimilation of surface displacements to improve geomechanical parameters of gas storage reservoirs C. Zoccarato et al. 10.1002/2015JB012090
- Soil hydrology: Recent methodological advances, challenges, and perspectives H. Vereecken et al. 10.1002/2014WR016852
- Comparison of microwave remote sensing and land surface modeling for surface soil moisture climatology estimation J. Dong et al. 10.1016/j.rse.2020.111756
- Unsaturated zone model complexity for the assimilation of evapotranspiration rates in groundwater modelling S. Gelsinari et al. 10.5194/hess-25-2261-2021
- Investigating the application of Kalman Filters for real-time accountancy in fusion fuel cycles H. Flynn & G. Larsen 10.1016/j.fusengdes.2022.113037
- Improving Robustness of Hydrologic Ensemble Predictions Through Probabilistic Pre‐ and Post‐Processing in Sequential Data Assimilation S. Wang et al. 10.1002/2018WR022546
- Error covariance calculation for forecast bias estimation in hydrologic data assimilation V. Pauwels & G. De Lannoy 10.1016/j.advwatres.2015.05.013
- Feasibility of Improving Groundwater Modeling by Assimilating Evapotranspiration Rates S. Gelsinari et al. 10.1029/2019WR025983
- Recursively updating the error forecasting scheme of a complementary modelling framework for improved reservoir inflow forecasts A. Gragne et al. 10.1016/j.jhydrol.2015.05.039
- On noise specification in data assimilation schemes for improved flood forecasting using distributed hydrological models S. Noh et al. 10.1016/j.jhydrol.2014.07.049
1 citations as recorded by crossref.
Saved (final revised paper)
Saved (preprint)
Latest update: 13 Dec 2024
