热带气象学报2025，Vol.41Issue(2)：200-210,11.DOI:10.16032/j.issn.1004-4965.2025.018
基于三维卷积的双偏振雷达定量降水估测研究
Research on Quantitative Precipitation Estimation Using Dual-Polarization Radar Based on 3D Convolution
摘要
Abstract
Heavy precipitation often triggers flooding disasters.Therefore,enhancing radar-based quantitative precipitation estimation(QPE)accuracy is critical for disaster mitigation.This study employs Guangzhou dual-polarization radar data and automatic weather station rainfall data to construct a four-dimensional dataset.Three three-dimensional convolutional QPE models—namely,3DPoly-QPENet,3DTime-QPENet,and 3DEcho-QPENet—were designed and evaluated through comparative experiments.Based on test-set performance assessments and validation with typical heavy rainfall cases,we draw the following condusions:(1)Compared to traditional three-dimensional datasets,the four-dimensional dataset better captures precipitation distribution characteristics and improves QPE fitting accuracy.(2)The three three-dimensional convolutional QPE models exhibit performance differences tied to their structural designs.Specifically,3DPoly-QPENet reduces the mean absolute error(MAE)by an average of 13％in moderate precipitation(15-20 mm·h-1)compared to the other two models.3DTime-QPENet achieves an average MAE reduction of 8.1％in high-intensity precipitation events(＞50 mm·h-1).3DEcho-QPENet shows the best global error balance,with an overall MAE reduction of 20.4％on average.(3)All three three-dimensional convolutional models surpass the traditional Z-R relationship method,reducing the root mean square error(RMSE)by an average of 46.6％,lowering MAE by 48.6％,and increasing the correlation coefficient(CC)by 21.4％.关键词
定量降水估测/双偏振雷达/四维数据集/三维卷积/深度学习Key words
quantitative precipitation estimation/dual-polarization radar/four-dimensional dataset/three-dimensional convolution/deep learning分类
大气科学引用本文复制引用
张毅,谢宸浩,陈雨欣,黎德波,张永华,熊梓立..基于三维卷积的双偏振雷达定量降水估测研究[J].热带气象学报,2025,41(2):200-210,11.基金项目
中国气象局智能气象观测技术重点开放实验室项目(ZNGC2024QN18) （ZNGC2024QN18）
广东省气象局科学技术研究项目(GRMC2023Q36、GRMC2023Z02) （GRMC2023Q36、GRMC2023Z02）
大城市智慧观测与数据应用创新团队项目共同资助 （）
