Convection-Allowing Ensemble Forecasts of Intense Rainfall and Hail: Case Study
Convective-allowing ensemble forecasts are analyzed for severe convection associated with short-duration intense rainfall and hail, which occurred in southern Jiangsu Province and Shanghai Region during the night of 28 April 2015. The data integration analysis is conducted at the initial time, using ARPS Data Analysis System (ADAS). The distribution of water substance is adjusted and more small-scale information is added to the initial field by using ADAS. Qualitative and quantitative verifications against Shanghai Nanhui dual-polarization radar observations are performed on the 12-14 h ensemble forecasts, including predicted reflectivity, differential reflectivity and hail. The results show that ADAS ensemble predicts the structure and location of the total and convective precipitation coverage more realistically compared with observations, and it also has higher forecast skill than the control (CTRL) ensemble. Differential reflectivity varies dramatically within a small distance, thus increasing the difficulty to make accurate predictions. In terms of the simulated differential reflectivity, the ADAS ensemble may produce higher values, but the overall position and intensity are closer to observations. The ADAS ensemble is more skillful for the prediction of large particles, which shows a better description of the microphysics-related features. Compared with ground hail observations and dual-polarization radar observations, the predicted high proba-bility hail location in ADAS ensemble is closer to the observations, which is of great significance to the prediction of hail falling area. Also, the ADAS ensemble improves the long-lead-time hail prediction and has higher confidence than the CTRL ensemble. The predicted dual-polarization variable has the advantage of distinguishing between intense rainfall and hail, providing an effective tool to evaluate the accuracy of the microphysical process description in the numerical model compared with the observation.