基于WRF模式的云南短时强降水物理量特征

为了解云南短时强降水发生前本地化中尺度WRF(Weather Research Forecast)模式输出结果的物理量特征及其对短时强降水预报的作用,使用WRF模式对2016年云南主汛期(6—8月)5次短时强降水过程进行模拟,利用模式输出的高时空分辨率资料计算5次过程中85个样本在短时强降水发生前6 h水汽类、动力类及不稳定条件类的部分物理量值,使用箱线图分析各物理量的分布特征及其与短时强降水的关系,应用经验累积分布函数图确定各物理量的阈值。研究表明,水汽类物理量样本数据值分布较为集中,随着短时强降水的临近数值逐渐增大;动力类的6 km垂直风切变中位数值及平均值随时间变化很小,所有时次的6 km垂直风切变阈值均低于12 m/s,表明短时强降水发生前有弱垂直风切变;不稳定条件类中对流有效位能样本数据的离散程度较大,对短时强降水无指示意义;LI指数、K指数和700 hPa假相当位温样本数据离散度较小,其中K指数中位数值、平均值及阈值的上下限在短时强降水发生前1 h有显著增大的特征,且数据集中度达到最高,大的K指数值与短时强降水有较好的对应关系。使用物理量阈值推算短时强降水落点的方法对云南本地化WRF模式短时强降水的预报性能有改进作用。

Analysis of physical quantity features of Yunnan short-time severe rainfall based on WRF model

In order to understand the physical features of the output results of localized mesoscale WRF (Weather Research Forecast) model before the occurrence of short-time severe rainfall in Yunnan Province and to reveal the effect of physical quantities on the prediction of short-time severe rainfall,WRF numerical prediction model was used to simulate five short-time severe rainfall events in the main flood season (June-August) of Yunnan in 2016.Using the high spatial and temporal resolution data output from the model,some physical quantities of water vapor,dynamic and unstable condition classes were calculated for 85 samples in five processes 6 hours before the occurrence of short-time severe rainfall.The distribution characteristics of physical quantities and their relationships with short-time severe rainfall are analyzed by box-line graph,and the thresholds of physical quantities are determined by empirical cumulative distribution function graph.Research shows that water vapor quantity sample values distribute intensively,and the values increase gradually with the approaching of short-time severe rainfall.The median and average values of 6 km vertical wind shear of dynamic class change little with time,and the threshold values of 6 km vertical wind shear of all times are lower than 12 m/s,which indicates that the vertical wind shear is weak before short-time severe rainfall.In the unstable condition class,the convective available potential energy (CAPE) sample data have greater dispersion,so CAPE has no instruction significance for short-time severe rainfall.The data of LI index,K index and 700 hPa pseudo equivalent potential temperature samples have less dispersion,and the upper and lower limits of the median value,average value and threshold of K index increase significantly one hour before the occurrence of short-time severe rainfall.The concentration of K index data is the highest one hour before short-time severe rainfall,and the larger K index value corresponds well with the short-time severe rainfall.The method of estimating the location of short-time severe rainfall by the physical quantity threshold value can improve the prediction performance of Yunnan localized WRF model to short-time severe rainfall.