摘要
利用欧洲中心(ECMWF)ERA-Interim再分析资料,通过分析2008—2017年山东冬半年不同降水相态(雨、雪和雨夹雪)下温度和位势厚度特征,统计得到8个降水相态判别因子(T 2 m、T 1000、T 975、T 950、T 925、T 850、H 850-700、H 1000-850),并给出每个判别因子降水相态阈值指标。然后利用8个判别因子和阈值建立降水相态判别方程和训练DNN模型,通过随机检验发现DNN法对雨、雪和雨夹雪的预报准确率分别提高1.9%、0.2%和21.6%;利用ECMWF细网格预报资料进行个例检验,雨、雪和雨夹雪共106站中判别方程法判别错误29站,DNN法判别错误14站,即DNN法的降水相态判别能力优于判别方程法,且明显提高了对雨夹雪的判别能力。
Based on ECMWF ERA-Interim reanalysis data,8 factors(T 2 m,T 1000,T 975,T 950,T 925,T 850,H 700-850 and H 850-1000)for identifying precipitation phases were obtained through analyzing the temperature and geopotential thickness of precipitation phases(rain,snow,sleet)in winter half year from 2008 to 2017 in Shandong Province,and the threshold indicators of the 8 factors were provided.The discriminant equation for precipitation phase identification was established and the deep learning DNN model was trained using the 8 factors and their threshold values,and the forecast accuracy of rain,snow and sleet increased by 1.9%,0.2%and 21.6%using DNN method through randomization test,respectively.The inspection using ECMWF fine grid model products indicated that among a total of 106 stations of rain,snow and sleet,the discriminant equation and DNN method carried out wrong identifications for 29 and 14 stations,respectively.The results show that the DNN method performed better than the discriminant equation,and in particular,it significantly improved the identification ability of sleet.
作者
朱文刚
李昌义
曲美慧
温晓培
ZHU Wengang;LI Changyi;QU Meihui;WEN Xiaopei(Shandong Institute of Meteorological Sciences,Jinan 250031,China;Jilin Institute of Meteorological Sciences,Changchun 130062,China)
出处
《干旱气象》
2020年第4期655-664,673,共11页
Journal of Arid Meteorology
基金
山东省重点研发计划项目(2016GSF120017)
山东省气象局青年科研基金项目(2016SDQN08)共同资助。
关键词
降水相态
判别方程
DNN法
温度阈值
厚度阈值
预报准确率
precipitation phase
discriminant equation
DNN method
temperature threshold
thickness threshold
forecast accuracy