Research on Quantitative Precipitation Estimation Using Dual-Polarization Radar Based on 3D Convolution
ZHANG Yi
XIE Chenhao
CHEN Yuxin
LI Debo
ZHANG Yonghua
XIONG Zili
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％.
Keywords：quantitative precipitation estimationdual-polarization radarfour-dimensional datasetthree-dimensional convolutiondeep learning
Publication Date：2025-04-30
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：11( 200-210 )