Explicit Numerical Simulations of Hailstorm Seeding with a Three–Moment Microphysics Cloud–Resolving Model: A Comparison of Two Operational Methodologies for Hail Suppression

Abstract. The global trend of increasing economic losses due to hailstorms sustains the relevance of hail suppression as a weather modification technique. However, quantifying the physical effectiveness of operational cloud seeding remains a significant challenge due to the inherent nonlinearity and complexity of convective processes.

In this study, we use a 3–dimensional numerical model, ARPS (Advanced Regional Prediction System), featuring a three–moment microphysical scheme to simulate the seeding of a supercell. The model is improved by implementing a two–moment aerosol microphysics that accounts for all the known scavenging processes and parameterizations of all four ice–nucleating modes. The prognostic equations for aerosol mixing ratio and number concentration in the air and in each hydrometeor category were calculated. Simulations were conducted at a high spatial resolution (500 m horizontal, 250 m vertical) over a 3–hour evolution period. This approach allows for a very explicit simulation of processes associated with cloud seeding.

Two operational seeding methodologies were investigated, RHSS (Republic Hydrometeorological Service of Serbia, 2023) and AB23 (Abshaev et al., 2023). The results indicate that both strategies effectively reduce hail–induced crop damage. Total hail kinetic energy decreased by 27 % (RHSS) and 17.9 % (AB23). Notably, the surface area of moderate risk (KE_flux > 100 J m^-2) was reduced by 36.6 % and 38.6 %, while high–risk areas (KE_flux > 300 J m^-2) saw a significant reduction of 66 % and 88 %, respectively.