Microphysical Perturbation Experiments and Ensemble Forecasts on Summertime Heavy Rainfall over Northern Taiwan

作     者：Chen, Jen-Ping Tsai, Tzu-Chin Tzeng, Min-Duan Liao, Chi-Shuin Kuo, Hung-Chi Hong, Jing-Shan 

作者机构：Natl Taiwan Univ Dept Atmospher Sci Taipei Taiwan Natl Taiwan Univ Int Degree Program Climate Change & Sustainable D Taipei Taiwan Acad Sinica Res Ctr Environm Changes Taipei Taiwan Cent Weather Bureau Taipei Taiwan 

出 版 物：《WEATHER AND FORECASTING》 (天气与预报)

年 卷 期：2022年第37卷第9期

页      面：1641-1659页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：MOST 105-2119-M-002-035 106-2119-M-002-016 07-2628-M-002 -016 108-2119-M-002 -022 108-2111-M-002 -015 -MY3 

主　　题：Cloud microphysics Ensembles Cloud parameterizations Mesoscale models 

摘      要：Microphysical perturbation experiments were conducted to investigate the sensitivity of convective heavy rain simulation to cloud microphysical parameterization and its feasibility for ensemble forecasts. An ensemble of 20 perturbation members differing in either the microphysics package or process treatments within a single scheme was applied to simulate 10 summer-afternoon heavy-rain convection cases. The simulations revealed substantial disagreements in the location and amplitude of peak rainfall among the microphysics-package and single-scheme members, with an overall spread of 57%-161%, 66%-161%, and 65%-149% of the observed average rainfall, maximum rainfall, and maximum intensity, respectively. The single-scheme members revealed that the simulation of heavy convective precipitation is quite sensitive to factors including ice-particle fall speed parameterization, aerosol type, ice particle shape, and size distribution representation. The microphysical ensemble can derive reasonable probability of occurrence for a location-specific heavy-rain forecast. Spatial-forecast performance indices up to 0.6 were attained by applying an optimal fuzzy radius of about 8 km for the warning-area coverage. The forecasts tend to be more successful for more organized convection. Spectral mapping methods were further applied to provide ensemble forecasts for the 10 heavy rainfall cases. For most cases, realistic spatial patterns were derived with spatial correlation up to 0.8. The quantitative performance in average rainfall, maximum rainfall, and maximum intensity from the ensembles reached correlations of 0.83, 0.84, and 0.51, respectively, with the observed values. Significance StatementHeavy rainfall from summer convections is stochastic in terms of intensity and location;therefore, an accurate deterministic forecast is often challenging. We designed perturbation experiments to explore weather forecasting models sensitivity to cloud microphysical parameterizations and the f