Improving thunderstorm and hail forecasting using an electrification model: Case study of the Moscow region

The increasing frequency and intensity of hazardous convective phenomena, such as heavy rain, hail, and thunderstorms, under climate change demands improved forecasting methods. However, existing models often fail to adequately account for the role of microphysical processes, particularly secondary ice formation, in the formation of cloud electrical structure and associated precipitation. The aim of this study is to establish quantitative relationships between the microphysical characteristics of convective clouds and lightning activity, as well as to assess the improvement in precipitation forecasts by explicitly accounting for the electrification mechanism. The study is based on numerical simulations using the WRF-ARW model with an implemented electrification module, incorporating inductive and non-inductive charge formation mechanisms, as well as parameterization of secondary ice generation by the Hallett-Mossop mechanism. Based on an analysis of a series of convective events in the Moscow region in July 2019, statistically significant correlations were identified between the ice fraction, particle size, temperature, and lightning discharge frequency. It was found that enabling the Hallett-Mossop mechanism improves key precipitation forecast metrics (BIAS, RMSE, MAE) by 24–25% compared to the standard WRF-ARW configuration. The results contribute to the development of physically based methods for short-term hazardous event forecasting by more accurately accounting for microphysical and electrical feedbacks in convective clouds.