Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

作     者：Jianbing Ma Xianghao Cui Nan Jiang 

作者机构：Chengdu University of Information TechnologyChengdu610225China Bournemouth UniversityBournemouthBH125BBUK 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第7期

页      面：1939-1949页

核心收录：

学科分类：0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 

基　　金：supported by Sichuan Provincial Key Research and Development Program(No.2021YFG0345,to J.Ma) the National Key Research and Development Program of China(No.2020YFA0608001,to J.Ma) 

主　　题：Deep learning meteorology precipitation nowcasting weather forecasting ZR formula 

摘      要：Sudden precipitations may bring troubles or even huge harm to people’s daily *** a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern ***,the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor(Z)and rainfall rate(R),which is typically estimated by location-dependent experiential formula and arguably ***,in this paper,we propose a deep learning-based method to model the ZR *** evaluate,we conducted our experiment with the Shenzhen precipitation *** proposed a combined method of deep learning and the ZR relationship,and compared it with a traditional ZR equation,a ZR equation with its parameters estimated by the least square method,and a pure deep learning *** experimental results show that our combined model performsmuch better than the equation-based ZRformula and has the similar performance with a pure deep learning nowcasting model,both for all level precipitation and heavy ones only.