Articles | Volume 14, issue 12
https://doi.org/10.5194/essd-14-5411-2022
© 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/essd-14-5411-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
OpenMRG: Open data from Microwave links, Radar, and Gauges for rainfall quantification in Gothenburg, Sweden
Jafet C. M. Andersson
CORRESPONDING AUTHOR
Swedish Meteorological and Hydrological Institute (SMHI), 601 76 Norrköping, Sweden
Jonas Olsson
Swedish Meteorological and Hydrological Institute (SMHI), 601 76 Norrköping, Sweden
Remco (C. Z.) van de Beek
Swedish Meteorological and Hydrological Institute (SMHI), 601 76 Norrköping, Sweden
Jonas Hansryd
Ericsson Research, Ericsson AB, Lindholmspiren 11, 412 56 Gothenburg, Sweden
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Cited
16 citations as recorded by crossref.
- Opportunistic Weather Sensing by Smart City Wireless Communication Networks J. Ostrometzky & H. Messer https://doi.org/10.3390/s24247901
- Stand-Alone, Affordable IoT Satellite Terminals and Their Opportunistic Use for Rain Monitoring J. Ostrometzky et al. https://doi.org/10.1109/IOTM.001.2200166
- On the Potential of Using Emerging Microwave Links for City Rainfall Monitoring C. Han et al. https://doi.org/10.1109/MCOM.001.2200975
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. https://doi.org/10.12688/openreseurope.16068.1
- Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza https://doi.org/10.1016/j.ejrh.2025.102390
- Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates J. Nielsen et al. https://doi.org/10.1016/j.atmosres.2024.107228
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors L. Petersson Wårdh et al. https://doi.org/10.5194/amt-19-461-2026
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. https://doi.org/10.3389/frsip.2024.1291878
- Spatio-Temporal Model for Predicting Multivariate Weather-Induced Attenuation in Wireless Networks D. Jacoby et al. https://doi.org/10.1109/TIM.2025.3555716
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. https://doi.org/10.12688/openreseurope.16068.2
- Technical note: A simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links E. Øydvin et al. https://doi.org/10.5194/hess-28-5163-2024
- A Review of Technical Aspects and Challenges in Opportunistic Rainfall Estimation Using Satellite and Terrestrial Microwave Links: How wireless infrastructure can be used for rainfall monitoring R. Nebuloni et al. https://doi.org/10.1109/MGRS.2025.3573645
- 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
- Hybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation P. Laon et al. https://doi.org/10.3390/s26020648
16 citations as recorded by crossref.
- Opportunistic Weather Sensing by Smart City Wireless Communication Networks J. Ostrometzky & H. Messer https://doi.org/10.3390/s24247901
- Stand-Alone, Affordable IoT Satellite Terminals and Their Opportunistic Use for Rain Monitoring J. Ostrometzky et al. https://doi.org/10.1109/IOTM.001.2200166
- On the Potential of Using Emerging Microwave Links for City Rainfall Monitoring C. Han et al. https://doi.org/10.1109/MCOM.001.2200975
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. https://doi.org/10.12688/openreseurope.16068.1
- Customizing large-scale hydrological models: Harnessing the open data realm for impactful local applications I. Pechlivanidis & J. Musuuza https://doi.org/10.1016/j.ejrh.2025.102390
- Merging weather radar data and opportunistic rainfall sensor data to enhance rainfall estimates J. Nielsen et al. https://doi.org/10.1016/j.atmosres.2024.107228
- Rainfall nowcasting models: state of the art and possible future perspectives D. De Luca et al. https://doi.org/10.1080/02626667.2025.2490780
- Have you ever seen the rain? Observing a record convective rainfall with national and local monitoring networks and opportunistic sensors L. Petersson Wårdh et al. https://doi.org/10.5194/amt-19-461-2026
- Intensity estimation after detection for accumulated rainfall estimation T. Weiss et al. https://doi.org/10.3389/frsip.2024.1291878
- Spatio-Temporal Model for Predicting Multivariate Weather-Induced Attenuation in Wireless Networks D. Jacoby et al. https://doi.org/10.1109/TIM.2025.3555716
- Precipitation Monitoring Using Commercial Microwave Links: Current Status, Challenges and Prospectives P. Zhang et al. https://doi.org/10.3390/rs15194821
- Data formats and standards for opportunistic rainfall sensors M. Fencl et al. https://doi.org/10.12688/openreseurope.16068.2
- Technical note: A simple feedforward artificial neural network for high-temporal-resolution rain event detection using signal attenuation from commercial microwave links E. Øydvin et al. https://doi.org/10.5194/hess-28-5163-2024
- A Review of Technical Aspects and Challenges in Opportunistic Rainfall Estimation Using Satellite and Terrestrial Microwave Links: How wireless infrastructure can be used for rainfall monitoring R. Nebuloni et al. https://doi.org/10.1109/MGRS.2025.3573645
- 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
- Hybrid Unsupervised–Supervised Learning Framework for Rainfall Prediction Using Satellite Signal Strength Attenuation P. Laon et al. https://doi.org/10.3390/s26020648
Saved (final revised paper)
Latest update: 17 Jun 2026
Short summary
This article presents data from three types of sensors for rain measurement, i.e. commercial microwave links (CMLs), gauges, and weather radar. Access to CML data is typically restricted, which limits research and applications. We openly share a large CML database (364 CMLs at 10 s resolution with true coordinates), along with 11 gauges and one radar composite. This opens up new opportunities to study CMLs, to benchmark algorithms, and to investigate how multiple sensors can best be combined.
This article presents data from three types of sensors for rain measurement, i.e. commercial...
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