摘要
Based on four reanalysis datasets including CMA-RA,ERA5,ERA-Interim,and FNL,this paper proposes an improved intelligent method for shear line identification by introducing a second-order zonal-wind shear.Climatic characteristics of shear lines and related rainstorms over the Southern Yangtze River Valley(SYRV)during the summers(June-August)from 2008 to 2018 are then analyzed by using two types of unsupervised machine learning algorithm,namely the t-distributed stochastic neighbor embedding method(t-SNE)and the k-means clustering method.The results are as follows:(1)The reproducibility of the 850 hPa wind fields over the SYRV using China’s reanalysis product CMARA is superior to that of European and American products including ERA5,ERA-Interim,and FNL.(2)Theory and observations indicate that the introduction of a second-order zonal-wind shear criterion can effectively eliminate the continuous cyclonic curvature of the wind field and identify shear lines with significant discontinuities.(3)The occurrence frequency of shear lines appearing in the daytime and nighttime is almost equal,but the intensity and the accompanying rainstorm have a clear diurnal variation:they are significantly stronger during daytime than those at nighttime.(4)Half(47%)of the shear lines can cause short-duration rainstorms(≥20 mm(3h)^(-1)),and shear line rainstorms account for one-sixth(16%)of the total summer short-duration rainstorms.Rainstorms caused by shear lines are significantly stronger than that caused by other synoptic forcing.(5)Under the influence of stronger water vapor transport and barotropic instability,shear lines and related rainstorms in the north and middle of the SYRV are stronger than those in the south.
作者
LIU Jin-qing
CHEN He
XU Jing-yu
刘金卿;陈鹤;徐靖宇(Guangzhou Institute of Tropical and Marine Meteorology/Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA,Guangzhou 510641 China;Hunan Meteorological Observatory/Hunan Key Laboratory of Meteorological Disaster Prevention and Reduction,Changsha 410118 China;Heavy Rain and Drought Flood Disasters in Plateau and Basin Key Laboratory of Sichuan,Chengdu 610072 China)
基金
Open Project Fund of Guangdong Provincial Key Laboratory of Regional Numerical Weather Prediction,CMA(J202009)
Heavy Rain and Drought-Flood Disasters in Plateau and Basin Key Laboratory of Sichuan Province(SZKT202005)
Innovation and Development Project of China Meteorological Administration(CXFZ2021J020)
Key Projects of Hunan Meteorological Service(XQKJ21A003,XQKJ21A004,XQKJ22A004)。
