Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning  

作　　者：Jiang HUANGFU Zhiqun HU Jiafeng ZHENG Lirong WANG Yongjie ZHU 

机构地区：[1]State Key Lab of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing 100081,China [2]Meteorological observatory of Jiangxi Province,Nanchang 330096,China [3]Hebei Meteorological Disaster Prevention and Environmental Meteorological Center,Shijiazhuang 050021,China [4]School of Atmospheric Sciences,Chengdu University of Information Technology,Chengdu 610225,China

出　　处：《Advances in Atmospheric Sciences》2024年第6期1147-1160,共14页大气科学进展（英文版）

基　　金：supported by National Key R&D Program of China(Grant No.2022YFC3003903);the S&T Program of Hebei(Grant No.19275408D),the Key-Area Research and Development Program of Guangdong Province(Grant No.2020B1111200001);the Key Project of Monitoring,Early Warning and Prevention of Major Natural Disasters of China(Grant No.2019YFC1510304);the Joint Fund of Key Laboratory of Atmosphere Sounding,CMA,and the Research Centre on Meteorological Observation Engineering Technology,CMA(Grant No.U2021Z05).

摘　　要：Accurate radar quantitative precipitation estimation(QPE)plays an essential role in disaster prevention and mitigation.In this paper,two deep learning-based QPE networks including a single-parameter network and a multi-parameter network are designed.Meanwhile,a self-defined loss function(SLF)is proposed during modeling.The dataset includes Shijiazhuang S-band dual polarimetric radar(CINRAD/SAD)data and rain gauge data within the radar’s 100-km detection range during the flood season of 2021 in North China.Considering that the specific propagation phase shift(KDP)has a roughly linear relationship with the precipitation intensity,KDP is set to 0.5°km^(-1 )as a threshold value to divide all the rain data(AR)into a heavy rain(HR)and light rain(LR)dataset.Subsequently,12 deep learning-based QPE models are trained according to the input radar parameters,the precipitation datasets,and whether an SLF was adopted,respectively.The results suggest that the effects of QPE after distinguishing rainfall intensity are better than those without distinguishing,and the effects of using SLF are better than those that used MSE as a loss function.A Z-R relationship and a ZH-KDP-R synthesis method are compared with deep learning-based QPE.The mean relative errors(MRE)of AR models using SLF are improved by 61.90%,51.21%,and 56.34%compared with the Z-R relational method,and by 38.63%,42.55%,and 47.49%compared with the synthesis method.Finally,the models are further evaluated in three precipitation processes,which manifest that the deep learning-based models have significant advantages over the traditional empirical formula methods.

关 键 词：polarimetric radar quantitative precipitation estimation deep learning single-parameter network multi-parameter network 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] P412.25[自动化与计算机技术—控制科学与工程]