Xintong Zhao

Radar quantitative precipitation estimation (QPE) is a crucial product for nowcasting and disaster warning. However, its accuracy is constrained by factors such as radar band, attenuation effects, and variations in the phase and microphysical properties of precipitation particles. Based on X-band phased-array radar data from Zhongshan City, Guangdong Province, this study compares and evaluates the QPE correction performance of three deep learning models: stacking ensemble learning, gated recurrent unit (GRU), and three-dimensional convolutional neural network (3D CNN). The aim is to explore the applicability of different model types under complex precipitation conditions. Data from August 2023 to August 2024 were used to construct the samples, with records from May 2024 held out as an independent test set and excluded from model training and hyperparameter tuning. Model performance was assessed under different radar combinations (three-radar, dual-radar, and single-radar configurations), temporal scales (minute and hourly), and precipitation intensities. The results show that: (1) at the minute scale, all three models improved the original QPE, reducing average relative error (RE) by approximately 24.6–29.5%, mean absolute error (MAE) by 23.2–27.7%, and root-mean-square error (RMSE) by 19.7–22.8%, while increasing correlation coefficient (CC) by approximately 20.4–20.9%. Specifically, GRU achieved the largest reduction in RE, stacking showed slight advantages in controlling MAE and RMSE, and 3D CNN and GRU showed similar improvements in CC. (2) At the hourly scale, the correction effect varied with precipitation intensity. In the light-to-moderate rainfall range ($0.1\le R<8.0\mathrm{mm}{\mathrm{h}}^{-1}$, where R denotes hourly rainfall), 3D CNN generally showed better error-control performance, whereas the advantage of GRU was less consistent among radar combinations. In the heavy-rainfall range ($R\ge 16.0\mathrm{mm}{\mathrm{h}}^{-1}$), stacking and GRU provided complementary value in some radar configurations, although model performance remained configuration dependent. (3) Case analysis shows that stacking can improve the original QPE at some extreme-precipitation stations, but correction performance in the extreme high-value range remains unstable, and GRU and 3D CNN are more prone to underestimation. Oriented toward operational applications, this study systematically evaluates the applicability and limitations of three model types under different scenarios while considering computational-resource constraints and timeliness requirements, thereby providing a reference for model selection and operational application in radar QPE correction.