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

作　　者：Kanghui ZHOU Jisong SUN Yongguang ZHENG Yutao ZHANG 

机构地区：[1]National Meteorological Center,Beijing 100081,China [2]Nanjing Joint Institute for Atmospheric Sciences,Nanjing 210000,China [3]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China

出　　处：《Advances in Atmospheric Sciences》2022年第9期1472-1486,共15页大气科学进展（英文版）

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)

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 humidity.This 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 precipitation.Basic variables forecasted by the highest-resolution model(HRES)of the European Centre for Medium-Range Weather Forecasts(ECMWF)were fed into QPFNet to forecast precipitation.Evaluation results show that QPFNet achieved better QPF performance than ECMWF HRES itself.The 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 precipitation.The 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 discussed.Using DL to extract features from basic meteorological variables can provide an important reference for QPF,and avoid some uncertainties of PSs.

关 键 词：deep learning quantitative precipitation forecast permutation importance numerical weather prediction 

分 类 号：P457.6[天文地球—大气科学及气象学]