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
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- 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
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- 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
- Pooled error variance and covariance estimation of sparse in situ soil moisture sensor measurements in agricultural fields in Flanders M. Hendrickx et al. 10.5194/soil-11-435-2025
- 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
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