Explicit numerical simulations of hailstorm seeding with a three–moment microphysics cloud–resolving model: a comparison of two operational methodologies for hail suppression

Increasing economic losses from hailstorms sustain the relevance of hail suppression as a weather modification technique. However, quantifying operational cloud seeding effectiveness remains challenging due to the inherent nonlinearity and complexity of convective processes. In this study, we use the 3D numerical model ARPS (Advanced Regional Prediction System), featuring a three-moment microphysical scheme, to simulate supercell seeding. The model was enhanced by implementing two-moment aerosol microphysics accounting for all known scavenging processes and parameterizations of all four ice-nucleating modes. Prognostic equations for aerosol mixing ratio and number concentration in air and each hydrometeor category were calculated. Simulations ran at high spatial resolution (500 m horizontal, 250 m vertical) over a 3 h evolution period. This approach enables detailed simulation of processes associated with cloud seeding. Two operational seeding strategies were investigated: RHSS (Republic Hydrometeorological Service of Serbia, 2023) and AB23 (Abshaev et al., 2023). Results indicate that both strategies modify surface hail characteristics by reducing kinetic energy, potentially mitigating crop damage. Total surface hail kinetic energy decreased by 27 % (RHSS) and 17.9 % (AB23). Notably, moderate-energy surface areas (KE flux >100 J m −2 ) decreased by 36.6 % and 38.6 %, while high-intensity areas (KE flux >300 J m −2 ) dropped by 66 % and 88 %, respectively. Nonparametric Wilcoxon signed-rank testing verified that both methods cause statistically significant changes ( p <0.05 and p <0.01) in surface hail diameter and kinetic energy flux throughout the storm lifecycle.