Mitigating hail overforecasting in the two-moment Milbrandt–Yau microphysics scheme (v2.25.2_beta_04) in WRF (v4.5.1) by incorporating the graupel spongy wet growth process (MY2_GSWG v1.0)
Nanjing Innovation Institute for Atmospheric Sciences, Chinese Academy of Meteorological Sciences–Jiangsu Meteorological Service, Nanjing 210041, China
Jiangsu Key Laboratory of Severe Storm Disaster Risk/Key Laboratory of Transportation Meteorology of CMA, Nanjing 210041, China
Hail forecasting using numerical models remains a challenge. In this study, we found that the commonly used graupel-to-hail conversion parameterization method led to hail overforecasting in heavy rainfall cases where no hail was observed. By incorporating the spongy wet growth process, we successfully mitigated hail overforecasting. The modified scheme also produced hail in real hail events. This research contributes to a better understanding of hail formation.
Hail forecasting using numerical models remains a challenge. In this study, we found that the...