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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40 citations as recorded by crossref.
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- Reconstructing and Nowcasting the Rainfall Field by a CML Network P. Zhang et al. 10.1029/2023EA002909
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- 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
- 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
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- 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
- Flood hazard assessment and the role of citizen science B. Sy et al. 10.1111/jfr3.12519
- Toward the camera rain gauge P. Allamano et al. 10.1002/2014WR016298
- Opportunistic remote sensing of rainfall using microwave links from cellular communication networks R. Uijlenhoet et al. 10.1002/wat2.1289
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. 10.1088/1748-9326/ac0fa6
- Quality Control for Crowdsourced Personal Weather Stations to Enable Operational Rainfall Monitoring L. de Vos et al. 10.1029/2019GL083731
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. 10.3390/rs15194821
- Crowdsourcing Methods for Data Collection in Geophysics: State of the Art, Issues, and Future Directions F. Zheng et al. 10.1029/2018RG000616
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- Use of autonomous transmission line-type electromagnetic sensors for classification of dry and wet periods at sub-hourly time intervals V. Mikešová et al. 10.1007/s10661-018-7059-7
- ExTraCT: Expediting Offloading Transfers Through Intervehicle Communication Transmissions P. Kolios et al. 10.1109/TITS.2014.2357221
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1 citations as recorded by crossref.
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