摘要
Short-duration heavy rainfall(SHR),as delineated by the National Meteorological Center of the China Me-teorological Administration,is characterized by hourly rainfall amounts no less than 20.0 mm.SHR is one of the most common convective weather phenomena that can cause severe damage.Short-range forecasting of SHR is an important part of operational severe weather prediction.In the present study,an improved objective SHR forecasting scheme was developed by adopting the ingredients-based methodology and using the fuzzy logic approach.The 1.0°×1.0°National Centers for Environmental Prediction(NCEP)final analysis data and the ordinary rainfall(0.1-19.9 mm h-1)and SHR observational data from 411 stations were used in the improved scheme.The best lifted index,the total precipitable water,the 925 hPa specific humidity(Q 925),and the 925 hPa divergence(DIV 925)were selected as predictors based on objective analysis.Continuously distributed membership functions of predictors were obtained based on relative frequency analysis.The weights of predictors were also objectively determined.Experiments with a typhoon SHR case and a spring SHR case show that the main possible areas could be captured by the improved scheme.Verification of SHR forecasts within 96 hours with NCEP global forecasts 1.0°×1.0°data initiated at 08:00 Beijing Time during the warm seasons in 2015 show the results were improved from both deterministic and probabilistic perspectives.This study provides an objectively feasible choice for short-range guidance forecasts of SHR.The scheme can be applied to other convective phenomena.
作者
TIAN Fu-you
XIA Kun
SUN Jian-hua
ZHENG Yong-guang
HUA Shan
田付友;夏坤;孙建华;郑永光;华珊(National Meteorological Center,Beijing 100081 China;State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG),Institute of Atmospheric Physics,Chinese Academy of Sciences(IAP/CAS),Beijing 100029 China;Key Laboratory of Cloud-Precipitation Physics and Severe Storms(LACS),Institute of Atmospheric Physics,Chinese Academy of Sciences(IAP/CAS),Beijing 100029 China)
基金
Key R&D Program of Xizang Autonomous Region(XZ202101ZY0004G)
National Natural Science Foundation of China(U2142202)
National Key R&D Program of China(2022YFC3004104)
Key Innovation Team of China Meteor-ological Administration(CMA2022ZD07)。
