Articles | Volume 25, issue 18
https://doi.org/10.5194/acp-25-10823-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-10823-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
19 Sep 2025
Research article |
| 19 Sep 2025
| 19 Sep 2025
Saharan dust linked to European hail events
Killian P. Brennan
CORRESPONDING AUTHOR
Institute for Atmospheric and Climate Science, ETH Zürich, Zurich, Switzerland
now at: Institute of Geography – Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
Lena Wilhelm
Institute of Geography – Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
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Weather Clim. Dynam., 6, 645–668, https://doi.org/10.5194/wcd-6-645-2025, https://doi.org/10.5194/wcd-6-645-2025, 2025
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We studied severe hailstorms that occurred in Switzerland on 28 June 2021 using a weather prediction model to understand how they evolved. We found that the storms moved toward areas with more storm energy. Hailfall was quickly followed by heavy rain. Just before the storms died out, the air feeding them stopped coming from near the ground. We also observed a delay between different types of precipitation forming in the incoming air.
Killian P. Brennan, Iris Thurnherr, Michael Sprenger, and Heini Wernli
EGUsphere, https://doi.org/10.5194/egusphere-2025-918, https://doi.org/10.5194/egusphere-2025-918, 2025
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Hailstorms can cause severe damage to homes, crops, and infrastructure. Using high-resolution climate simulations, we tracked thousands of hailstorms across Europe to study future changes. Large hail will become more frequent, hail-covered areas will expand, and extreme hail combined with heavy rain will double. These shifts could increase risks for communities and businesses, highlighting the need for better preparedness and adaptation.
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, https://doi.org/10.5194/amt-17-2539-2024, 2024
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This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.
Killian P. Brennan, Michael Sprenger, André Walser, Marco Arpagaus, and Heini Wernli
Weather Clim. Dynam., 6, 645–668, https://doi.org/10.5194/wcd-6-645-2025, https://doi.org/10.5194/wcd-6-645-2025, 2025
Short summary
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We studied severe hailstorms that occurred in Switzerland on 28 June 2021 using a weather prediction model to understand how they evolved. We found that the storms moved toward areas with more storm energy. Hailfall was quickly followed by heavy rain. Just before the storms died out, the air feeding them stopped coming from near the ground. We also observed a delay between different types of precipitation forming in the incoming air.
Lucas Pfister, Lena Wilhelm, Yuri Brugnara, Noemi Imfeld, and Stefan Brönnimann
Weather Clim. Dynam., 6, 571–594, https://doi.org/10.5194/wcd-6-571-2025, https://doi.org/10.5194/wcd-6-571-2025, 2025
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Our work compares different machine learning approaches for creating long-term classifications of daily atmospheric circulation patterns using input data from surface meteorological observations. Our comparison reveals that a feedforward neural network performs best at this task. Using this model, we present a daily reconstruction of a commonly used weather type classification for central Europe that dates back to 1728.
Killian P. Brennan, Iris Thurnherr, Michael Sprenger, and Heini Wernli
EGUsphere, https://doi.org/10.5194/egusphere-2025-918, https://doi.org/10.5194/egusphere-2025-918, 2025
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Hailstorms can cause severe damage to homes, crops, and infrastructure. Using high-resolution climate simulations, we tracked thousands of hailstorms across Europe to study future changes. Large hail will become more frequent, hail-covered areas will expand, and extreme hail combined with heavy rain will double. These shifts could increase risks for communities and businesses, highlighting the need for better preparedness and adaptation.
Lena Wilhelm, Cornelia Schwierz, Katharina Schröer, Mateusz Taszarek, and Olivia Martius
Nat. Hazards Earth Syst. Sci., 24, 3869–3894, https://doi.org/10.5194/nhess-24-3869-2024, https://doi.org/10.5194/nhess-24-3869-2024, 2024
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In our study we used statistical models to reconstruct past hail days in Switzerland from 1959–2022. This new time series reveals a significant increase in hail day occurrences over the last 7 decades. We link this trend to increases in moisture and instability variables in the models. This time series can now be used to unravel the complexities of Swiss hail occurrence and to understand what drives its year-to-year variability.
Martin Lainer, Killian P. Brennan, Alessandro Hering, Jérôme Kopp, Samuel Monhart, Daniel Wolfensberger, and Urs Germann
Atmos. Meas. Tech., 17, 2539–2557, https://doi.org/10.5194/amt-17-2539-2024, https://doi.org/10.5194/amt-17-2539-2024, 2024
Short summary
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This study uses deep learning (the Mask R-CNN model) on drone-based photogrammetric data of hail on the ground to estimate hail size distributions (HSDs). Traditional hail sensors' limited areas complicate the full HSD retrieval. The HSD of a supercell event on 20 June 2021 is retrieved and contains > 18 000 hailstones. The HSD is compared to automatic hail sensor measurements and those of weather-radar-based MESHS. Investigations into ground hail melting are performed by five drone flights.
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Short summary
In this study, we discovered that natural dust carried into Europe significantly increases the likelihood of hailstorms. By analyzing dust data, weather records, and hail reports, we found that moderate dust levels lead to more frequent hail, while very high or low dust amounts reduce it. Adding dust information into statistical models improved forecasting skills.
In this study, we discovered that natural dust carried into Europe significantly increases the...
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