MINI REVIEW article
Front. Environ. Sci.
Sec. Atmosphere and Climate
This article is part of the Research TopicOutcome of the 4th European Hail Workshop 2024: Opening the discussionView all 5 articles
Advances and challenges in hail research: Report from the 4th European Hail Workshop
Provisionally accepted- 1Institute of Meteorology and Climate Research Troposphere Research (IMKTRO), Karlsruhe Institute of Technology (KIT),, Karlsruhe, Germany
- 2Institute of Meteorology and Climate Research (IMKTRO), Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
- 3Institute of Geography, Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland
- 4Meteo Swiss, Locarno-Monti, Switzerland
- 5Institute of Environmental Social Sciences and Geography, University of Freiburg, Freiburg, Germany
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Hailstorms cause substantial damage to buildings, crops, vehicles, and infrastructure in many regions worldwide. Despite notable progress in recent years, hail remains insufficiently understood and poorly represented in numerical weather prediction models and risk assessments. The 4th European Hail Workshop (2024) showcased advances in detection, forecasting, climatology, and impact assessment of hail, while highlighting key challenges that remain. Progress in remote sensing, weather prediction, and seamless forecasting has improved early detection of hail events, extended forecast lead times, and enhanced warning capabilities. Field campaigns and laboratory experiments are yielding new insights into hailstone characteristics, hail formation processes, and impacts. Studies of storm dynamics and microphysics emphasized the complex interactions of processes involved across a wide range of temporal and spatial scales. Finally, artificial intelligence and machine learning are opening new avenues for hail detection, prediction, and risk modeling, marking a shift toward more integrated and innovative approaches in hail research.
Keywords: Hail, Hailstorms, Hail damage, Hail climatology, Hail detection, hail forecasting, Microphysics, AI/ML
Received: 04 Sep 2025; Accepted: 31 Oct 2025.
Copyright: © 2025 Kunz, Mohr, Martius, Hering and Schröer. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Michael Kunz, michael.kunz@kit.edu
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