基于CBAM U-Net模型的成都X波段相控阵雷达降水估计OA

The frequent occurrence of heavy precipitation events poses severe challenges to urban disaster pre-vention and mitigation.High-resolution quantitative precipitation estimation(QPE)products are of great impor-tance for monitoring intense rainfall processes,while X-band phased array radars(X-PAR),delivering detection data with high spatiotemporal resolution,provide a robust foundation for advanced QPE research.Due to the limitations of traditional radar-based QPE methods,such as insufficient nonlinear mapping capabilities and the lack of dynamic adaptive learning mechanisms,a minute-scale rainfall QPE method based on the CBAM U-Net model is proposed.This model enhances the learning capability for key precipitation regions and feature chan-nels by incorporating the Channel and Spatial Attention Module(CBAM)and optimizes the temporal alignment between radar and ground rain gauge observations by introducing a temporal dimension to the model input.X-PAR data from four radars in Chengdu's plain area during July-September 2023-2024 were used to construct training and testing datasets.Additionally,the training process was optimized through the integration of learning rate warm-up and cosine annealing strategies,which accelerated the convergence speed and improved the model's robustness.Evaluation metrics,including regression indices(CC,RMSE,RMAE,RMB)and classification metrics(POD,FAR,CSI),were applied to compare the performance of the CBAM U-Net against Attention U-Net,U-Net,and two Z-R methods.Results show that deep learning models significantly outperform traditional Z-R methods in both error metrics and stability.Among the three models,CBAM U-Net achieves the best perfor-mance(CC=0.665,RMSE=0.331 mm·min-1,RMAE=44.651%,RMB=7.324%),followed by Attention U-Net,while U-Net shows significant underestimation.In multi-threshold classification evaluations,CBAM U-Net achieves CSI of 0.655 and 0.485 at thresholds of 0.34 mm·min-1(corresponding to 20 mm·h-1)and 0.67 mm·min ⁻ ¹(corresponding to 40 mm·h-1)respectively,which are notably higher than those of the Atten-tion U-Net(0.609,0.408)and U-Net(0.537,0.160).Case studies further verify that CBAM U-Net has a stronger capability to capture temporal features.During the"pulse-type"precipitation event on 10 September 2024,CBAM U-Net accurately identifies the peaks and troughs of precipitation curves and effectively character-izes the temporal variation features of precipitation,performing better than U-Net and Attention U-Net.During the stable precipitation process on 29 September 2024,CBAM U-Net successfully captures the overall trend of precipitation changes but shows insufficient sensitivity to minor fluctuations within the range of 0.1～0.2 mm·min-1.The CBAM module effectively improves the model's capability to capture temporal characteristics of heavy precipitation via its dual attention mechanism.However,due to the inherent systematic smoothing effect of deep learning models,notable underestimation still occurs for extreme heavy precipitation events with minute rainfall rates≥1.17 mm·min-1.