Articles | Volume 26, issue 3
https://doi.org/10.5194/hess-26-775-2022
© Author(s) 2022. 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-26-775-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
11 Feb 2022
Research article |
| 11 Feb 2022
| 11 Feb 2022
Citizen rain gauges improve hourly radar rainfall bias correction using a two-step Kalman filter
Department of Water Resources Engineering, Kasetsart University, P.O. Box 1032, Bangkok 10900, Thailand
Monton Methaprayun
Department of Water Resources Engineering, Kasetsart University, P.O. Box 1032, Bangkok 10900, Thailand
Thom Bogaard
Department of Water Management, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Gerrit Schoups
Department of Water Management, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
Marie-Claire Ten Veldhuis
Department of Water Management, Delft University of Technology, P.O. Box 5048, 2600 GA Delft, the Netherlands
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Cited
14 citations as recorded by crossref.
- Evaluation of factors affecting the quality of citizen science rainfall data in Akaki catchment, Addis Ababa, Ethiopia H. Tedla et al. 10.1016/j.jhydrol.2022.128284
- Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning M. Rosenhoover et al. 10.3390/s25123719
- Recording Rainfall Intensity: Has an Optimum Method Been Found? D. Dunkerley 10.3390/w15193383
- Towards the development of a citizens’ science-based acoustic rainfall sensing system M. Alkhatib et al. 10.1016/j.jhydrol.2024.130973
- Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia H. Tedla et al. 10.1016/j.ejrh.2022.101273
- Optimizing rainfall prediction in central thailand with weather radar and machine learning during the monsoon N. Mahavik et al. 10.1007/s12665-025-12149-9
- Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand N. Mahavik et al. 10.1007/s10668-024-05778-w
- An Urban Acoustic Rainfall Estimation Technique Using a CNN Inversion Approach for Potential Smart City Applications M. Alkhatib et al. 10.3390/smartcities6060139
- A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow S. Ahmed et al. 10.3390/hydrology9080133
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
- 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
- Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
- Integrating WRF forecasts at different scales for pluvial flood forecasting using a rainfall threshold approach and a real-time flood model A. Young et al. 10.1016/j.jhydrol.2025.132891
- Downscaling the Z–R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand P. Mapiam et al. 10.2166/wst.2023.056
14 citations as recorded by crossref.
- Evaluation of factors affecting the quality of citizen science rainfall data in Akaki catchment, Addis Ababa, Ethiopia H. Tedla et al. 10.1016/j.jhydrol.2022.128284
- Improving Doppler Radar Precipitation Prediction with Citizen Science Rain Gauges and Deep Learning M. Rosenhoover et al. 10.3390/s25123719
- Recording Rainfall Intensity: Has an Optimum Method Been Found? D. Dunkerley 10.3390/w15193383
- Towards the development of a citizens’ science-based acoustic rainfall sensing system M. Alkhatib et al. 10.1016/j.jhydrol.2024.130973
- Evaluation of WRF model rainfall forecast using citizen science in a data-scarce urban catchment: Addis Ababa, Ethiopia H. Tedla et al. 10.1016/j.ejrh.2022.101273
- Optimizing rainfall prediction in central thailand with weather radar and machine learning during the monsoon N. Mahavik et al. 10.1007/s12665-025-12149-9
- Investigate ensemble machine learning models to reduce the daily Mean field bias of radar rainfall estimates derived from ZR relationships in the sub-river basins in the middle of Thailand N. Mahavik et al. 10.1007/s10668-024-05778-w
- An Urban Acoustic Rainfall Estimation Technique Using a CNN Inversion Approach for Potential Smart City Applications M. Alkhatib et al. 10.3390/smartcities6060139
- A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow S. Ahmed et al. 10.3390/hydrology9080133
- Advancing river monitoring using image-based techniques: challenges and opportunities S. Manfreda et al. 10.1080/02626667.2024.2333846
- 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
- Threshold-based flood early warning in an urbanizing catchment through multi-source data integration: Satellite and citizen science contribution H. Tedla et al. 10.1016/j.jhydrol.2024.131076
- Integrating WRF forecasts at different scales for pluvial flood forecasting using a rainfall threshold approach and a real-time flood model A. Young et al. 10.1016/j.jhydrol.2025.132891
- Downscaling the Z–R relationship and bias correction solution for flash flood assessment in a data-scarce basin, Thailand P. Mapiam et al. 10.2166/wst.2023.056
Latest update: 28 Aug 2025
Short summary
The density of rain gauge networks plays an important role in radar rainfall bias correction. In this work, we aimed to assess the extent to which daily rainfall observations from a dense network of citizen scientists improve the accuracy of hourly radar rainfall estimates in the Tubma Basin, Thailand. Results show that citizen rain gauges significantly enhance the performance of radar rainfall bias adjustment up to a range of about 40 km from the center of the citizen rain gauge network.
The density of rain gauge networks plays an important role in radar rainfall bias correction. In...
