Articles | Volume 18, issue 10
https://doi.org/10.5194/essd-18-7455-2026
© Author(s) 2026. 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-18-7455-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
12 Oct 2026
Data description article |
| 12 Oct 2026
| 12 Oct 2026
The OpenSat4Weather dataset: Ku-band satellite link data for precipitation monitoring
Institute of Electronics, Computer and Telecommunication Engineering (IEIIT) of the National Research Council of Italy (CNR), Milan, Italy
Maximilian Graf
Department of Hydrometeorology, Deutscher Wetterdienst, Offenbach, Germany
Greta Cazzaniga
Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212 CEA-CNRS-UVSQ, Université Paris-Saclay, IPSL, 91191 Gif-sur-Yvette, France
François Mercier
Sereno/HD Rain, 198 avenue de France, Paris, France
Maxime Turko
Sereno/HD Rain, 198 avenue de France, Paris, France
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Elia Covi, Giacomo Roversi, and Roberto Nebuloni
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-160, https://doi.org/10.5194/essd-2026-160, 2026
Revised manuscript accepted for ESSD
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OpenRainER is an open-source dataset containing two years of precipitation measurements from the Emilia-Romagna region in Italy, collected using commercial microwave links, weather radars and rain gauges. OpenRainER aims to promote research on opportunistic sensors for rainfall monitoring. The regional context allows tackling challenges from complex orography, and the accompanying article describes the data to facilitate its use.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
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Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Greta Cazzaniga, Anastasia Akakpo-Numado, Patrick Brockmann, Adrien Burq, Mathieu Vrac, and Davide Faranda
Nat. Hazards Earth Syst. Sci., 26, 4741–4752, https://doi.org/10.5194/nhess-26-4741-2026, https://doi.org/10.5194/nhess-26-4741-2026, 2026
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Extreme weather events are becoming more frequent and severe, creating a strong need for rapid and reliable information. We developed an open tool that automatically detects and tracks heatwaves, cold spells, heavy rain, and strong winds across Europe, both in real time and in past decades. By comparing current events with long historical records, the tool shows how unusual an event is and reveals clear increases in heatwaves, while other hazards show more mixed changes.
Elia Covi, Giacomo Roversi, and Roberto Nebuloni
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2026-160, https://doi.org/10.5194/essd-2026-160, 2026
Revised manuscript accepted for ESSD
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OpenRainER is an open-source dataset containing two years of precipitation measurements from the Emilia-Romagna region in Italy, collected using commercial microwave links, weather radars and rain gauges. OpenRainER aims to promote research on opportunistic sensors for rainfall monitoring. The regional context allows tackling challenges from complex orography, and the accompanying article describes the data to facilitate its use.
Christian Chwala, Aart Overeem, Erlend Øydvin, Louise Petersson Wårdh, Jochen Seidel, Maximilian Graf, Bas Walraven, Elia Covi, Hai Victor Habi, Martin Fencl, Lotte de Vos, Filippo Giannetti, Amy Green, Tess O’Hara, Nico Blettner, Tom Keel, Georges Schutz, Abbas El Hachem, Nicholas Illich, Julius Polz, Taoufiq Shit, Lukáš Kaleta, Damaris Zulkarnaen, and Vojtěch Bareš
EGUsphere, https://doi.org/10.5194/egusphere-2025-5438, https://doi.org/10.5194/egusphere-2025-5438, 2026
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Data from so called opportunistic sensors, devices that have not been designed to provide reliable weather data, can offer valuable rainfall information. But good data processing is crucial. For this task, we created the OpenSense software ecosystem, featuring new specialized tools built on a shared foundation. These new tools allow us to collaboratively advance research and improve the operational usage of opportunistic rainfall data to provide more accurate rainfall maps.
Erlend Øydvin, Elia Covi, Maximilian Graf, and Christian Chwala
EGUsphere, https://doi.org/10.5194/egusphere-2025-6371, https://doi.org/10.5194/egusphere-2025-6371, 2025
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We developed an open-source tool to combine weather radar and commercial microwave link data, tested on large, public datasets. Merging these measurements produced more accurate rainfall estimates than radar alone, and we show which methods work best in different situations to support improved rainfall monitoring and applications.
Louise Gelbart, Laurent Barthès, François Mercier-Tigrine, Aymeric Chazottes, and Cécile Mallet
Atmos. Meas. Tech., 18, 351–370, https://doi.org/10.5194/amt-18-351-2025, https://doi.org/10.5194/amt-18-351-2025, 2025
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In this paper, we present and evaluate a new method for the quantitative estimation of precipitation from a low-cost sensor. Based on previous work measuring the attenuation of an electromagnetic signal from a broadcast television satellite, we make this approach more accurate so it can be easily deployed and used operationally in areas where rainfall measurements are critical for applications like flood monitoring. In this article, the method is validated in France and applied in Côte d'Ivoire.
Erlend Øydvin, Maximilian Graf, Christian Chwala, Mareile Astrid Wolff, Nils-Otto Kitterød, and Vegard Nilsen
Hydrol. Earth Syst. Sci., 28, 5163–5171, https://doi.org/10.5194/hess-28-5163-2024, https://doi.org/10.5194/hess-28-5163-2024, 2024
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Two simple neural networks are trained to detect rainfall events using signal loss from commercial microwave links. Whereas existing rainfall event detection methods have focused on hourly resolution reference data, this study uses weather radar and rain gauges with 5 min and 1 min temporal resolutions, respectively. Our results show that the developed neural networks can detect rainfall events with a higher temporal precision than existing methods.
Maximilian Graf, Andreas Wagner, Julius Polz, Llorenç Lliso, José Alberto Lahuerta, Harald Kunstmann, and Christian Chwala
Atmos. Meas. Tech., 17, 2165–2182, https://doi.org/10.5194/amt-17-2165-2024, https://doi.org/10.5194/amt-17-2165-2024, 2024
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Commercial microwave links (CMLs) can be used for rainfall retrieval. The detection of rainy periods in their attenuation time series is a crucial processing step. We investigate the usage of rainfall data from MSG SEVIRI for this task, compare this approach with existing methods, and introduce a novel combined approach. The results show certain advantages for SEVIRI-based methods, particularly for CMLs where existing methods perform poorly. Our novel combination yields the best performance.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
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Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
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Short summary
We describe the OpenSat4Weather public dataset, which provides time series of received power from 215 TV-SAT satellite microwave links located in Southern France over a five-month observation period and at 1-min resolution. These data can be processed to gather indirect rainfall intensity measurements. OpenSat4Weather also includes concurrent data from operational rain gauges and weather radars, and ERA5 0°C isotherm height data, to derive the wet fraction of the satellite-to-ground path.
We describe the OpenSat4Weather public dataset, which provides time series of received power...
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