Study on the Reason for Overestimation of a Snowfall Case by WSM6 Cloud Microphysical Scheme over North China
The GRAPES regional operational model in the National Meteorological Centre significantly overestimated the snowfall amount over North China that occurred in 29-30 November 2019. In this paper, the simulated results from the operational WSM6 cloud microphysics scheme are compared with those from Liu-Ma scheme and the ERA5 reanalysis data to investigate the possible reasons. The results show that during this snowfall, the sedimentations of ice crystals and snow were the main contribution in WSM6 scheme, while the precipitation of Liu-Ma scheme was mainly through the sedimentations of snow and graupel, and ice crystals produced less precipitation. The WSM6 scheme evidently underestimated the liquid water content in the atmosphere and the ice crystal content was the largest composition of the ice-phase particles, followed by the snow content. These features were significantly different from the ERA5 data and the Liu-Ma scheme, and the latter two were in good agreement. Compared with the Liu-Ma scheme, the WSM6 scheme had a higher ice crystal content in the lower layer of the model and a larger average ice crystal falling speed, and their combination made ice crystal precipitation become an important contribution to the formation of this snowfall case. The average snow falling speed in the WSM6 scheme was greater than that of Liu-Ma scheme, which was the main reason why the column snow content was small and the precipitation of snow was more than that of the Liu-Ma scheme. In the WSM6 scheme, the deposition/sublimation process of ice crystals dominated the ice-phase microphysical processes so that the sublimation processes of snow and graupel and the condensation process of cloud water were obviously insufficient. This was the main reason for more ice crystals, less snow and cloud water in WSM6 scheme. The sensitivity test for the ice crystal deposition/sublimation process (SVI) revealed that the SVI conversion rate was positively correlated to surface precipitation, and took on a “seesaw” relationship with the column cloud water content. When the SVI conversion rate was reduced, the ground snowfall tended to be significantly reduced and the column cloud water content increased significantly.