| 摘要: |
| 利用常规观测资料、加密自动站大风资料和数值模式预报产品对比分析了 2018年4月4—5日(简称“4·4”过程)和2018年4月12—13日(简称“4·12”过程) 发生在四川盆地的两次范围、强度有明显差异的混合性大风天气过程，探讨两次过程发生的天气学异同条件及数值模式对这类大风天气的预报能力。分析表明：两次过程形成梯度风的基础条件较为相似，但影响系统斜压性、低层动力辐合、热力条件、不稳定条件有所不同；近地层全球模式风场预报产品和集合预报极端大风指数产品对两次过程大风的强度和范围有一定的提示作用；全球模式产品能正确预报两次过程的影响系统、动力条件、热力条件和要素场，能提示“4·4”过程更强的对流特征。综合分析实况观测资料、数值模式风场预报产品和要素预报产品，能在短期预报时效内提高对这类大风过程范围及强度的预测能力。 |
| 关键词: 混合性大风；对比分析；实况资料；数值模式产品 |
| DOI： |
| 投稿时间：2019-09-24 |
| 基金项目: |
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| Comparative Analysis of Synoptic Cause and Numerical Model Prediction Capability of Two Mixed Gale Processes |
| PU Jiguang,YUAN Meng,KANG Lan |
| (Sichuan Meteorological Service Centre, Chengdu 610072 , China ;Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province,Chengdu 610072 ,China;Sichuan Meteorological Observatory, Chengdu 610072 , China) |
| Abstract: |
| Two mixed gale processes were analyzed based on the conventional observation data, gale data from encrypted automatic stations, and numerical model prediction products. These gale processes occurred from April 4 to 5, 2018 (referred to as the "4·4" process) and April 12 to 13, 2018 (referred to as the "4·12" process) in Sichuan Basin and were different in intensity. The similarities and differences of synoptic conditions of the processes and the numerical model were analyzed to acquire gale forecast ability. The results shows that the gradient wind conditions of the two gale processes are similar while the baroclinity, low-level dynamic convergence, thermal conditions and instability conditions are different. Near-surface wind field forecast products and ensemble forecast extreme wind products are indicating the strength and extent of the two gale processes. Global model products can correctly predict the impact system, dynamic conditions, thermal conditions and factor fields of two gale processes, and indicate the stronger convective characteristics of the "4·4" process. The comprehensive analysis of the actual observation data, numerical model wind field prediction products and factor prediction products can improve the forecast ability of the intensity and range of such processes within the short-term forecasting. |
| Key words: mixed gale； comparative analysis； actual observation data； numerical model products |
