基于双分支复合小波注意力网络的雷达定量降水估计OA

Current deep learning-based QPE methods using radar reflectivity factors mostly adopt global mapping strategies,which to some extent limits the models' ability to analyze local precipitation features.To this end,this study proposes a Dual-Branch Composite Wavelet Attention UNet(DCWA-UNet)model.The model is mainly improved from the following two aspects.Firstly,a hybrid architecture consisting of a dual-branch encoder(main branch+simplified convolutional downsampling branch)and a feature aggre-gation subnetwork is designed,which enables the end-to-end mapping from radar volume scan data to station precipitation intensity,thereby constructing a station-centered sample system.Secondly,a com-posite wavelet attention module(CWAM)is introduced to enhance the model's representation capability for radar echoes through multi-scale feature decomposition and dynamic weight allocation.Meanwhile,a weighted mean squared error loss function is adopted to emphasize the gradient contribution of moderate-to-high precipitation.Using ground-based precipitation observation and radar observation data in the Si-chuan Basin during the summers of 2019-2021,a dataset containing 4020 samples is constructed for model training and testing,and comparative experiments are conducted with deep learning models such as Sim-VP.The results show that DCWA-UNet achieves obvious comprehensive performance advantages under different precipitation intensities,with particularly significant improvements in critical success index(CSI)and mean absolute error(MAE)within the precipitation intensity below 30 mm·h-1.For precipitation in-tensities of[5,10)mm·h-1,the CSI of DCWA-UNet is significantly higher than that of SimVP and oth-er comparative models,and the MAE is reduced by 4.9％compared to SimVP;for precipitation intensities of[10,30]mm·h-1,the CSI is improved by 4.0％and the MAE is reduced by 4.0％compared to Sim-VP.Moreover,the false alarm rate is the lowest among all comparative models.