Evaluation of Riming in the P3‐2ice Microphysics Scheme: A Case Study of Cold‐Air Outbreak Snowfall in Eastern China

Abstract

Abstract Using the multi‐frequency radar suite, ground‐based observations, and the Weather Research and Forecasting (WRF) model with the Predicted Particle Properties microphysics scheme with the version of two‐ice category (P3‐2ice), we study the characteristics of a cold‐air outbreak (CAO) snowfall event on 6–7 December 2024. The cloud system is shallow convection, with the microphysical characteristics differing between the first and second snowfall periods. The WRF model reproduces the synoptic‐scale weather system and mesoscale structure well, but underestimates the event‐averaged snow rate and rime mass fraction by approximately 52% and 24%, respectively. Within the riming process, the collection of cloud water by ice is the dominant pathway. However, the fixed collection efficiency decouples riming from wind shear. To study the impact of collection efficiency on snow rate, we increase the efficiency as a simplified approach to represent the potential effect of shear‐generated turbulence on riming. The results of sensitivity experiments reveal that the snow rate exhibits a positive correlation with collection efficiency, but with contrasting behaviors between the two snowfall periods. In the first period, persistent severe underestimation occurs; in the second period, overestimation occurs when efficiency is high, and the rime mass fraction shows a nonlinear response to efficiency. These results indicate that a fixed collection efficiency is inadequate and a dynamic environment‐constrained efficiency is required. The research provides new insights into how to improve the riming process parameterization, contributing to a better understanding of the dynamic and microphysical characteristics of CAO snowfall.