Abstract:Doppler weather radar observations play a vital role in hail identification, but the spatio-temporal coverage of single-source radar data is limited and the timeliness and accuracy of traditional identification methods are not satisfying. To address this issue, this paper proposes a hail identification method based on satellite and radar data fusion. Utilizing the spatio-temporal complementarity between satellite and radar data, the method combines threshold characteristics of satellite and radar observations before and after hailfall to achieve efficient multi-source data fusion and identification through deep learning algorithms. Experiment results indicate that the proposed method effectively integrated satellite and radar data, with the mean average precision of the YOLO v7 model is 0.9083. It successfully identified hail-affected regions, providing important references for early warning of hail weather. Notably, in areas where radar data were susceptible to terrain occlusion, the method significantly improved the problem of inaccurate identification of hail areas caused by poor data quality, demonstrating high practical application values.