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.
Kunz et al. (Fri,) studied this question.