Diagnosis of moist vorticity and moist divergence for a heavy precipitation event in Southwestern China
详细信息 查看全文
- 作者：Gang Li ; Daoyong Yang ; Xiaohua Jiang ; Jing Pan…
- 关键词：moist vorticity ; moist divergence ; heavy precipitation ; southwestern China
- 刊名：Advances in Atmospheric Sciences
- 出版年：2017
- 出版时间：January 2017
- 年：2017
- 卷：34
- 期：1
- 页码：88-100
- 全文大小：
- 刊物主题：Atmospheric Sciences; Meteorology; Geophysics/Geodesy;
- 出版者：Science Press
- ISSN：1861-9533
- 卷排序：34
文摘
A regional heavy precipitation event that occurred over Sichuan Province on 8–9 September 2015 is analyzed based on hourly observed precipitation data obtained from weather stations and NCEP FNL data. Two moist dynamic parameters, i.e., moist vorticity (mζ) and moist divergence (mδ), are used to diagnose this heavy precipitation event. Results show that the topography over southwestern China has a significant impact on the ability of these two parameters to diagnose precipitation. When the impact of topography is weak (i.e., low altitude), mζ cannot exactly depict the location of precipitation in the initial stage of the event. Then, as the precipitation develops, its ability to depict the location improves significantly. In particular, mζ coincides best with the location of precipitation during the peak stage of the event. Besides, the evolution of the mζ center shows high consistency with the evolution of the precipitation center. For mδ, although some false-alarm regions are apparent, it reflects the location of precipitation almost entirely during the precipitation event. However, the mδ center shows inconsistency with the precipitation center. These results suggest that both mζ and mδ have a significant ability to predict the location of precipitation. Moreover, mζ has a stronger ability than mδ in terms of predicting the variability of the precipitation center. However, when the impact of topography is strong (i.e., high altitude), both of these two moist dynamic parameters are unable to depict the location and center of precipitation during the entire precipitation event, suggesting their weak ability to predict precipitation over complex topography.