HailTrack—Improving Radar-Based Hailfall Estimates by Modeling Hail Trajectories
Jordan Brook, Alain Protat, Joshua Soderholm, Jacob T. Carlin, Hamish A. McGowan, Robert A. Warren
Abstract
Abstract A spatial mismatch between radar-based hail swaths and surface hail reports is commonly noted in meteorological literature. The discrepancy is partly due to hailstone advection and melting between detection aloft and observation at the ground. This study aims to mitigate this problem by introducing a model named HailTrack, which estimates hailfall at the surface using radar observations. The model operates by detecting, tracking, and collating hailstone trajectories using dual-polarized, dual-Doppler radar retrievals. Notable improvements in hailfall forecasts were observed through the use of HailTrack, and initializing the model with radar retrievals of hail differential reflectivity H DR was found to produce the most accurate hailfall estimates. The analysis of a case study in Brisbane, Australia, demonstrated that trajectory modeling significantly improved the correlation between hail swaths and hail-related insurance losses, increasing Heidke skill scores from 0.48 to 0.58. The accumulated kinetic energy of hailstone impacts also showed some skill in identifying areas that were exposed to particularly severe hailfall. Other unique impact estimates are presented, such as hailstone advection information and hailstone impact angle statistics. The potential to run the model in real time and produce short-term (10–15 min) nowcasts is also introduced. Model applications include improving radar-based hail climatologies, validating hail detection techniques and insurance claims data, and providing real-time hail impact maps to improve public awareness of hail risk.