Articles | Volume 25, issue 7
https://doi.org/10.5194/hess-25-4061-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4061-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
13 Jul 2021
Research article |
| 13 Jul 2021
| 13 Jul 2021
A climatological benchmark for operational radar rainfall bias reduction
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, Delft, the Netherlands
Claudia Brauer
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
Klaas-Jan van Heeringen
Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, Delft, the Netherlands
Hidde Leijnse
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Aart Overeem
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, De Bilt, the Netherlands
Albrecht Weerts
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
Operational Water Management & Early Warning, Department of Inland Water Systems, Deltares, Delft, the Netherlands
Remko Uijlenhoet
Hydrology and Quantitative Water Management Group, Wageningen University & Research, Wageningen, the Netherlands
Department of Water Management, Delft University of Technology, Delft, the Netherlands
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Cited
14 citations as recorded by crossref.
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- Enhancing the accuracy of weather radar heavy rainfall estimates in mountainous regions using combined radar quality indices M. Methaprayun et al. 10.1016/j.jhydrol.2025.133907
- Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning R. Imhoff et al. 10.1029/2021WR031591
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- Strange Storms: Rainfall Extremes From the Remnants of Hurricane Ida (2021) in the Northeastern US J. Smith et al. 10.1029/2022WR033934
- Time-independent bias correction methods compared with gauge adjustment methods in improving radar-based precipitation estimates K. Yousefi et al. 10.1080/02626667.2023.2248108
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- Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter Y. Luo et al. 10.1016/j.jhydrol.2024.131819
- A climatological benchmark for operational radar rainfall bias reduction R. Imhoff et al. 10.5194/hess-25-4061-2021
13 citations as recorded by crossref.
- Improving Treatment of Noise Specification of Kalman Filtering for State Updating of Hydrological Models: Combining the Strengths of the Interacting Multiple Model Method and Cubature Kalman Filter Y. Sun et al. 10.1029/2022WR033635
- Long-term multi-source precipitation estimation with high resolution (RainGRS Clim) A. Jurczyk et al. 10.5194/amt-16-4067-2023
- Reanalysis of multi-year high-resolution X-band weather radar observations in Hamburg F. Burgemeister et al. 10.5194/essd-16-2317-2024
- Mitigating blade erosion damage through nowcast-driven erosion-safe mode control N. Barfknecht et al. 10.1088/1742-6596/2767/3/032001
- Enhancing the accuracy of weather radar heavy rainfall estimates in mountainous regions using combined radar quality indices M. Methaprayun et al. 10.1016/j.jhydrol.2025.133907
- Large‐Sample Evaluation of Radar Rainfall Nowcasting for Flood Early Warning R. Imhoff et al. 10.1029/2021WR031591
- Development of the consider cubature Kalman filter for state estimation of hydrological models with parameter uncertainty Y. Sun et al. 10.1016/j.jhydrol.2023.130080
- Improving the Forecast Performance of Hydrological Models Using the Cubature Kalman Filter and Unscented Kalman Filter Y. Sun et al. 10.1029/2022WR033580
- The Dutch real-time gauge-adjusted radar precipitation product A. Overeem et al. 10.5194/essd-17-4715-2025
- Strange Storms: Rainfall Extremes From the Remnants of Hurricane Ida (2021) in the Northeastern US J. Smith et al. 10.1029/2022WR033934
- Time-independent bias correction methods compared with gauge adjustment methods in improving radar-based precipitation estimates K. Yousefi et al. 10.1080/02626667.2023.2248108
- Merging with crowdsourced rain gauge data improves pan-European radar precipitation estimates A. Overeem et al. 10.5194/hess-28-649-2024
- Enhancing physically-based flood forecasts through fusion of long short-term memory neural network with unscented Kalman filter Y. Luo et al. 10.1016/j.jhydrol.2024.131819
1 citations as recorded by crossref.
Latest update: 29 Oct 2025
Short summary
Significant biases in real-time radar rainfall products limit the use for hydrometeorological forecasting. We introduce CARROTS (Climatology-based Adjustments for Radar Rainfall in an OperaTional Setting), a set of fixed bias reduction factors to correct radar rainfall products and to benchmark other correction algorithms. When tested for 12 Dutch basins, estimated rainfall and simulated discharges with CARROTS generally outperform those using the operational mean field bias adjustments.
Significant biases in real-time radar rainfall products limit the use for hydrometeorological...
