Radial Interference Correction Based on Random Forest for Dual-Polarization Radar
Abstract：To further improve the quality of radar echo data, this study used 106 volume scans from Guangzhou's new generation dual-polarization Doppler weather radar in June 2024 as research samples. The sample data were divided into training and validation sets using a stratified sampling method. A radial interference identification model was constructed using the random forest algorithm. The results showed that when the model contained 69 decision trees and the size of the feature subset used at each node split was 3, the classification performance on the validation set was optimal, with a comprehensive evaluation index reaching 96.62%. In addition, a radial basis function (RBF)-based interpolation method was proposed to compensate for the removed radial interference echoes, maintaining the spatial continuity of radar reflectivity data and avoiding possible error amplification caused by simple linear interpolation.
Keywords：Dual-polarization weather radarRandom forestRadial interferenceRadial basis function
Publication Date：2025-08-20
Online Publishing Date：2025-09-15(First online date of this platform, not the publication date of the document)
Pages：5( 105-109 )