The Impact of Autoconversion Parameterizations of Cloud Droplet to Raindrop on Numerical Simulations of a Meiyu Front Heavy Rainfall Event

Abstract

This study analyzes the different impacts of autoconversion of cloud droplets to raindrops (ACR) in a Meiyu front rainfall event by comparing two simulations using different parameterizations (KK00 and LD04) in the Weather Research and Forecasting (WRF) model. The Meiyu frontal clouds are further classified into stratiform and deep-convective cloud categories, and the precipitation and microphysical characteristics of the two simulations are compared with a budget analysis of raindrops. The simulated precipitation, radar composite reflectivity distribution, and rain rate evolution are overall consistent with observations while precipitation is overestimated, especially in the rainfall centers. The intensity and vertical structure of the ACR process between the two simulations are significantly different. The ACR rate in LD04 is larger than that in KK00 and there are two peak heights in LD04 but only one in KK00. Accretion of droplets by raindrops (CLcr), melting of ice-phase particles (ML), evaporation of raindrops (VDrv), and accretion of raindrops by ice-phase particles (CLri) are the dominant pathways to raindrop production. Limited distributional differences can be found in both the deep-convective and stratiform clouds between the two simulations during the growth stage of the Meiyu event. Stronger ACR in LD04 results in less cloud droplet content (Lc), more raindrop content (Lr), and larger raindrop number concentration (Nr) and the effect of ACR on Nr is greater than that on Lr. The ACR process also impacts other microphysical processes indirectly, and the influences vary in the two cloud categories. Less CLcr (especially), ML, and VDrv content, caused by stronger ACR, lead to less raindrop production in the LD04 deep-convective clouds, which is different from stratiform clouds, and finally correct the overestimated rainfall center to better match the observations.

Keywords

• autoconversion parameterization
• Meiyu
• source and sink terms
• WRF model