WRF Optimization for Hail Risk: Coupling Environmental Clustering with Genetic Algorithms

Hailstorms are among the most damaging convective weather events globally, leading to significant socioeconomic impacts on infrastructure, agriculture, and property. Effective hail prediction and hindcasting require a robust understanding of the environmental conditions under which hail forms. Synoptic and mesoscale atmospheric patterns play a critical role in convective phenomena such as hail formation, with variations in these patterns being closely linked to hailstorm development. The classification of these patterns is essential for identifying region-specific environmental conditions, which is crucial for optimizing modeling strategies and improving the accuracy of hail predictions. Consequently, hail prediction requires an integrated approach that considers multiscale processes, spanning synoptic-scale conditions, mesoscale characteristics, and convective parameters.

This study proposes a framework that combines atmospheric conditions favourable to hail occurrence, accounting for spatial and seasonal variability in hail frequency and physical drivers such as orographic features with numerical model optimisation to improve region-specific hail simulation.

To this end, we couple the selected hail-prone environments in Europe, categorised into clusters, with a Genetic Algorithm (GA) designed to optimize the configuration of the Weather Research and Forecasting (WRF) model for simulating various hail conditions. The GA systematically evaluates different combinations of WRF physics schemes to identify those most effective at reproducing observed hail events. By applying the derived optimized WRF configurations to representative cases within each cluster, we assess whether different environmental settings require tailored modelling configurations for accurate hail simulation. This integrated approach has the potential to reveal important links between local terrain, synoptic-scale patterns, and model performance.

The integration of this clustering with model optimization offers a scalable and efficient pathway for improving hail simulations. By linking atmospheric conditions for hail formation with optimised WRF configurations, this framework enables more streamlined, region-specific hail simulations and a better understanding of hail formation processes, ultimately enhancing hail prediction capabilities and hail risk assessments.