Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning

作     者：Kanghui ZHOU Jisong SUN Yongguang ZHENG Yutao ZHANG Kanghui ZHOU;Jisong SUN;Yongguang ZHENG;Yutao ZHANG

作者机构：National Meteorological CenterBeijing 100081China Nanjing Joint Institute for Atmospheric SciencesNanjing 210000China State Key Laboratory of Severe WeatherChinese Academy of Meteorological SciencesBeijing 100081China 

出 版 物：《Advances in Atmospheric Sciences》 (大气科学进展（英文版）)

年 卷 期：2022年第39卷第9期

页      面：1472-1486页

核心收录：

学科分类：07[理学] 070601[理学-气象学] 0706[理学-大气科学] 

基　　金：the financial support of the National Key Research and Development Program (Grant No. 2017YFC1502000) the National Natural Science Foundation of China (Key Program, 91937301) 

主　　题：deep learning quantitative precipitation forecast permutation importance numerical weather prediction 

摘      要：The quantitative precipitation forecast(QPF)performance by numerical weather prediction(NWP)methods depends fundamentally on the adopted physical parameterization schemes(PS).However,due to the complexity of the physical mechanisms of precipitation processes,the uncertainties of PSs result in a lower QPF performance than their prediction of the basic meteorological variables such as air temperature,wind,geopotential height,and *** study proposes a deep learning model named QPFNet,which uses basic meteorological variables in the ERA5 dataset by fitting a non-linear mapping relationship between the basic variables and *** variables forecasted by the highest-resolution model(HRES)of the European Centre for Medium-Range Weather Forecasts(ECMWF)were fed into QPFNet to forecast *** results show that QPFNet achieved better QPF performance than ECMWF HRES *** threat score for 3-h accumulated precipitation with depths of 0.1,3,10,and 20 mm increased by 19.7%,15.2%,43.2%,and 87.1%,respectively,indicating the proposed performance QPFNet improved with increasing levels of *** sensitivities of these meteorological variables for QPF in different pressure layers were analyzed based on the output of the QPFNet,and its performance limitations are also *** DL to extract features from basic meteorological variables can provide an important reference for QPF,and avoid some uncertainties of PSs.