Articles | Volume 17, issue 11
https://doi.org/10.5194/hess-17-4701-2013
© 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-17-4701-2013
© Author(s) 2013. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| Highlight paper
| 
28 Nov 2013
Research article | Highlight paper |
| 28 Nov 2013
Rainfall estimation using moving cars as rain gauges – laboratory experiments
E. Rabiei
Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz Universität Hannover, Hanover, Germany
U. Haberlandt
Institute of Water Resources Management, Hydrology and Agricultural Hydraulic Engineering, Leibniz Universität Hannover, Hanover, Germany
M. Sester
Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Hanover, Germany
D. Fitzner
Institute of Cartography and Geoinformatics, Leibniz Universität Hannover, Hanover, Germany
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- The Car as an Ambient Sensing Platform [Point of View] E. Massaro et al. 10.1109/JPROC.2016.2634938
- A novel quality control model of rainfall estimation with videos – A survey based on multi-surveillance cameras X. Wang et al. 10.1016/j.jhydrol.2021.127312
- Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links G. Scognamiglio et al. 10.3390/s24216944
- Fast Bayesian Regression Kriging Method for Real‐Time Merging of Radar, Rain Gauge, and Crowdsourced Rainfall Data P. Yang & T. Ng 10.1029/2018WR023857
- Flood hazard assessment and the role of citizen science B. Sy et al. 10.1111/jfr3.12519
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- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. 10.1088/1748-9326/ac0fa6
- Cars as a Diffuse Network of Road-Environment Monitoring Nodes M. Allegretti & S. Bertoldo 10.4236/wsn.2014.69018
- Evaluating weather impact on vehicles: a systematic review of perceived precipitation dynamics and testing methodologies W. Pao et al. 10.1088/2631-8695/ad2033
- From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services I. Garcia‐Marti et al. 10.1002/joc.7757
- Estimation of raindrop size distribution and rain rate with infrared surveillance camera in dark conditions J. Lee et al. 10.5194/amt-16-707-2023
- Evaluating rain gauge precipitation measurements using a giant rain gauge as a benchmark A. Petroselli & S. Grimaldi 10.1007/s00704-025-05540-w
- Harnessing Crowdsourced Data and Prevalent Technologies for Atmospheric Research N. David 10.1007/s00376-019-9022-0
- Hydrometeorological Monitoring Using Opportunistic Sensing Networks in the Amsterdam Metropolitan Area L. de Vos et al. 10.1175/BAMS-D-19-0091.1
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. 10.1016/j.ejrh.2021.100883
- A discrete droplet method for modelling thin film flows A. S． Bharadwaj et al. 10.1016/j.apm.2022.08.001
- Reconstructing and Nowcasting the Rainfall Field by a CML Network P. Zhang et al. 10.1029/2023EA002909
- Estimation of precipitation fields from 1-minute rain gauge time series – comparison of spatial and spatio-temporal interpolation methods D. Fitzner & M. Sester 10.1080/13658816.2015.1040022
- Areal rainfall estimation using moving cars – computer experiments including hydrological modeling E. Rabiei et al. 10.5194/hess-20-3907-2016
- Advancing Opportunistic Sensing in Hydrology: A Novel Approach to Measuring Rainfall With Ordinary Surveillance Cameras S. Jiang et al. 10.1029/2018WR024480
- Recording Rainfall Intensity: Has an Optimum Method Been Found? D. Dunkerley 10.3390/w15193383
- A Bayesian deep learning approach for video-based estimation and uncertainty quantification of urban rainfall intensity F. Zheng et al. 10.1016/j.jhydrol.2024.131706
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- Toward the camera rain gauge P. Allamano et al. 10.1002/2014WR016298
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- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. 10.3390/rs15194821
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- Application of machine learning-based post-processing to improve crowd-sourced urban rainfall categorizations M. Hussain et al. 10.1016/j.acags.2025.100255
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