Evaluating Raindrop Self-Collection and Breakup Parameterizations in Heavy Rainfall Using Double-Moment Bulk Microphysics Schemes

Abstract This study uses three double-moment bulk microphysics schemes to simulate four rainstorm events in China, aiming to evaluate the simulated raindrop size distribution (RSD) in heavy rainfall. Both the Thompson and Milbrandt–Yau schemes exhibit nearly constant near-surface raindrop mean diameter D m when the rainwater content exceeds 2 g m −3 , noticeably diverging from D m values retrieved by polarimetric radar. The variability in D m between deep and shallow convection also steadily decreases from the top of warm-cloud layer to the surface. The National Severe Storms Laboratory (NSSL) scheme shows an unrealistic increase in the near-surface D m values with increasing rainwater content. By investigating the relative contribution to the total raindrop number transfer rate, the raindrop self-collection and breakup processes are identified to control all the simulated RSDs in heavy rainfall. In these schemes, the raindrop self-collection/breakup rate is anomalously positively correlated with the rainwater content. The self-collection/breakup efficiency E c is a simple function of D m in the schemes. In Thompson and Milbrandt–Yau, E c is positive below a cutoff D m value, indicating net raindrop self-collection, and negative above it, indicating net raindrop breakup. In contrast, E c is always nonnegative in NSSL, suggesting net raindrop self-collection rather than breakup. Two sensitivity experiments are designed by swapping the E c values between Thompson and NSSL. Simulated RSD characteristics from the two schemes are effectively exchanged. Such changes in self-collection and breakup settings directly affect extreme rainfall forecasting by altering raindrop fall velocities and affecting other warm-rain processes, with an impact of up to 30% on the maximum hourly rainfall forecasts. Significance Statement Accurate parameterization of cloud microphysical processes and representation of raindrop size distribution characteristics are essential for high-accuracy quantitative precipitation forecast in numerical models. This study evaluates the simulated raindrop size distribution in multiple rainstorm events and the ability of key microphysical process parameterizations in heavy rainfall. Deficiencies in cloud parameterizations and raindrop size distribution simulations are revealed and found to obviously affect the quantitative forecasting of heavy rainfall. The findings also provide valuable insights into improving heavy rainfall forecasting from the perspective of cloud microphysics parameterization.