Articles | Volume 11, issue 7
https://doi.org/10.5194/amt-11-4465-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-11-4465-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Jul 2018
Research article |
| 26 Jul 2018
| 26 Jul 2018
Rainfall retrieval with commercial microwave links in São Paulo, Brazil
Manuel F. Rios Gaona
CORRESPONDING AUTHOR
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University & Research, 6708 PB Wageningen, the Netherlands
Aart Overeem
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University & Research, 6708 PB Wageningen, the Netherlands
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, 3731 GA De Bilt, the Netherlands
Timothy H. Raupach
Environmental Remote Sensing Laboratory, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
Current affiliation: Institute of Geography, and Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Hidde Leijnse
R&D Observations and Data Technology, Royal Netherlands Meteorological Institute, 3731 GA De Bilt, the Netherlands
Remko Uijlenhoet
Hydrology and Quantitative Water Management Group, Department of Environmental Sciences, Wageningen University & Research, 6708 PB Wageningen, the Netherlands
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Cited
31 citations as recorded by crossref.
- Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01458-6
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. https://doi.org/10.1016/j.ejrh.2021.100883
- Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics L. de Vos et al. https://doi.org/10.1175/JTECH-D-18-0197.1
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. https://doi.org/10.1029/2019WR026255
- Comparing GPROF V7 and commercial microwave link precipitation estimates with DPR in the Netherlands, Sri Lanka and Nigeria L. Bogerd et al. https://doi.org/10.1080/02626667.2026.2686442
- Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods K. Song et al. https://doi.org/10.1109/JSTARS.2020.3021555
- Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment R. Nebuloni et al. https://doi.org/10.3390/s22093218
- A rain estimation model based on microwave signal attenuation measurements in the city of Ioannina, Greece V. Christofilakis et al. https://doi.org/10.1002/met.1932
- Development of Capacitive Rain Gauge for Marine Environment H. Chai et al. https://doi.org/10.1155/2021/6639668
- Precipitation Estimates From Commercial Microwave Links: Practical Approaches to Wet-Antenna Correction J. Pastorek et al. https://doi.org/10.1109/TGRS.2021.3110004
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges C. Chwala & H. Kunstmann https://doi.org/10.1002/wat2.1337
- Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing A. Sakkas et al. https://doi.org/10.3390/electronics13050840
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. https://doi.org/10.3390/s22124395
- Multi-source estimation of rainfall using opportunistic sensors in urban areas in Burkina Faso J. Bonkoungou et al. https://doi.org/10.2166/wst.2025.160
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. https://doi.org/10.5194/amt-15-485-2022
- Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature V. Christofilakis et al. https://doi.org/10.3390/sym12091440
- Rainfall Nowcasting Using Commercial Microwave Links R. Imhoff et al. https://doi.org/10.1029/2020GL089365
- AI-Enhanced Rainfall Retrieval Using Commercial Microwave Links in 6G-IoT Networks: Advances, Challenges, and Opportunities M. Wang et al. https://doi.org/10.1109/JIOT.2025.3593645
- On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas X. Zheng et al. https://doi.org/10.1016/j.atmosres.2022.106289
- A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links X. Zheng et al. https://doi.org/10.1029/2023EF003718
- Analysis of Wet Antenna Losses on 11.843 GHz Slant Path in Nigeria and Comparison with Some Tropical Climates A. Abdulrahman https://doi.org/10.1590/2179-10742022v21i2259250
- Synoptic climatology of weather parameters associated with tropical cyclone events in the coastal areas of Bay of Bengal M. Badhan et al. https://doi.org/10.1007/s00704-022-04284-1
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. https://doi.org/10.1088/1748-9326/ac0fa6
- Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links G. Scognamiglio et al. https://doi.org/10.3390/s24216944
- From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services I. Garcia‐Marti et al. https://doi.org/10.1002/joc.7757
- A measurement campaign to assess sources of error in microwave link rainfall estimation T. van Leth et al. https://doi.org/10.5194/amt-11-4645-2018
- Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko https://doi.org/10.1016/j.jqsrt.2019.106692
- Uncertainties in discharge predictions based on microwave link rainfall estimates in a small urban catchment J. Pastorek et al. https://doi.org/10.1016/j.jhydrol.2022.129051
- Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy G. Roversi et al. https://doi.org/10.5194/amt-13-5779-2020
31 citations as recorded by crossref.
- Using Machine Learning Techniques for Rainfall Estimation Based on Microwave Links of Mobile Telecommunication Networks E. Kamtchoum et al. https://doi.org/10.1007/s42979-022-01458-6
- Rainfall estimates from opportunistic sensors in Germany across spatio-temporal scales M. Graf et al. https://doi.org/10.1016/j.ejrh.2021.100883
- Rainfall Estimation Accuracy of a Nationwide Instantaneously Sampling Commercial Microwave Link Network: Error Dependency on Known Characteristics L. de Vos et al. https://doi.org/10.1175/JTECH-D-18-0197.1
- Deep Learning for an Improved Prediction of Rainfall Retrievals From Commercial Microwave Links J. Pudashine et al. https://doi.org/10.1029/2019WR026255
- Comparing GPROF V7 and commercial microwave link precipitation estimates with DPR in the Netherlands, Sri Lanka and Nigeria L. Bogerd et al. https://doi.org/10.1080/02626667.2026.2686442
- Experimental Study of Detecting Rainfall Using Microwave Links: Classification of Wet and Dry Periods K. Song et al. https://doi.org/10.1109/JSTARS.2020.3021555
- Comparison of CML Rainfall Data against Rain Gauges and Disdrometers in a Mountainous Environment R. Nebuloni et al. https://doi.org/10.3390/s22093218
- A rain estimation model based on microwave signal attenuation measurements in the city of Ioannina, Greece V. Christofilakis et al. https://doi.org/10.1002/met.1932
- Development of Capacitive Rain Gauge for Marine Environment H. Chai et al. https://doi.org/10.1155/2021/6639668
- Precipitation Estimates From Commercial Microwave Links: Practical Approaches to Wet-Antenna Correction J. Pastorek et al. https://doi.org/10.1109/TGRS.2021.3110004
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Commercial microwave link networks for rainfall observation: Assessment of the current status and future challenges C. Chwala & H. Kunstmann https://doi.org/10.1002/wat2.1337
- Harnessing the Radio Frequency Power Level of Cellular Terminals for Weather Parameter Sensing A. Sakkas et al. https://doi.org/10.3390/electronics13050840
- A Review on Rainfall Measurement Based on Commercial Microwave Links in Wireless Cellular Networks B. Lian et al. https://doi.org/10.3390/s22124395
- Multi-source estimation of rainfall using opportunistic sensors in urban areas in Burkina Faso J. Bonkoungou et al. https://doi.org/10.2166/wst.2025.160
- Rainfall retrieval algorithm for commercial microwave links: stochastic calibration W. Wolff et al. https://doi.org/10.5194/amt-15-485-2022
- Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature V. Christofilakis et al. https://doi.org/10.3390/sym12091440
- Rainfall Nowcasting Using Commercial Microwave Links R. Imhoff et al. https://doi.org/10.1029/2020GL089365
- AI-Enhanced Rainfall Retrieval Using Commercial Microwave Links in 6G-IoT Networks: Advances, Challenges, and Opportunities M. Wang et al. https://doi.org/10.1109/JIOT.2025.3593645
- On the potential of commercial microwave link networks for high spatial resolution rainfall monitoring in urban areas X. Zheng et al. https://doi.org/10.1016/j.atmosres.2022.106289
- A Statistical Approach to Retrieve Rainfall Intensity by Attenuation Measurements From Commercial Microwave Links X. Zheng et al. https://doi.org/10.1029/2023EF003718
- Analysis of Wet Antenna Losses on 11.843 GHz Slant Path in Nigeria and Comparison with Some Tropical Climates A. Abdulrahman https://doi.org/10.1590/2179-10742022v21i2259250
- Synoptic climatology of weather parameters associated with tropical cyclone events in the coastal areas of Bay of Bengal M. Badhan et al. https://doi.org/10.1007/s00704-022-04284-1
- Application of commercial microwave links (CMLs) attenuation for quantitative estimation of precipitation M. Pasierb et al. https://doi.org/10.1002/met.2218
- Tropical rainfall monitoring with commercial microwave links in Sri Lanka A. Overeem et al. https://doi.org/10.1088/1748-9326/ac0fa6
- Deep Learning for Opportunistic Rain Estimation via Satellite Microwave Links G. Scognamiglio et al. https://doi.org/10.3390/s24216944
- From proof‐of‐concept to proof‐of‐value: Approaching third‐party data to operational workflows of national meteorological services I. Garcia‐Marti et al. https://doi.org/10.1002/joc.7757
- A measurement campaign to assess sources of error in microwave link rainfall estimation T. van Leth et al. https://doi.org/10.5194/amt-11-4645-2018
- Comprehensive thematic T-matrix reference database: a 2017–2019 update M. Mishchenko https://doi.org/10.1016/j.jqsrt.2019.106692
- Uncertainties in discharge predictions based on microwave link rainfall estimates in a small urban catchment J. Pastorek et al. https://doi.org/10.1016/j.jhydrol.2022.129051
- Commercial microwave links as a tool for operational rainfall monitoring in Northern Italy G. Roversi et al. https://doi.org/10.5194/amt-13-5779-2020
Saved (final revised paper)
Latest update: 03 Nov 2026
Short summary
Rainfall estimates from commercial microwave links were obtained for the city of Sao Paulo (Brazil). The results show the potential of such networks as complementary rainfall measurements for more robust networks (e.g. radars, gauges, satellites).
Rainfall estimates from commercial microwave links were obtained for the city of Sao Paulo...
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