Simulation of Different Microphysical Schemes and Latent Heat Effects on Extreme Precipitation of Typhoon Haikui (2023)

The remnant of Typhoon Haikui (2311) caused extreme precipitation in the Guangdong-Hong Kong-Macao Greater Bay Area, breaking multiple precipitation records in Shenzhen and Hong Kong. In this study, three microphysical schemes, LIUMA, WSM6, and THOM, were selected for a simulation based on CMA-MESO V6.0. Forecast performance, errors in the prediction of various meteorological elements, microphysical variables, and their changes were compared across the different schemes. Additionally, latent heat sensitivity tests were conducted for further analysis. The LIUMA run exhibited a slightly higher threat score (TS) and a higher bias than the other two runs. In the region experiencing heavy precipitation, all three runs showed peaks of significant snow and graupel totals near the 0 ℃ layer, and peaks of rainwater near 3 km. The contribution of ice-phase particles varied among the schemes, with WSM6 contributing mostly graupel and THOM and LIUMA contributing mostly snow. The LIUMA run demonstrated a significantly larger net latent heat release near the 0 ℃ layer compared to the WSM6 and THOM runs and forecasted stronger warm core structures and remnants of a landfalling tropical cyclone (LTC). All three runs predicted a warm-core structure located to the east of the low-level center of the LTC remnant, which slowed the westward movement of the remnant and facilitated the maintenance of heavy precipitation. Compared to the WSM6 and THOM experiments, the LIUMA experiment released significantly more latent heat near the 0 ℃ layer and forecast a stronger warm-core structure and LTC remnants, leading to more false alarms. Latent heat sensitivity tests revealed the importance of latent heat feedback from microphysical processes, indicating that a decrease in latent heat could lead to a rapid westward weakening of the LTC remnant and an increase in forecast error. The model also exhibited significant uncertainty in its extreme precipitation forecasts, which could affect the overall forecast field through latent heat release.