Deep Learning-Based Identification and Tracking of Mesoscale Convective Systems Using Merged Precipitation Products

Mesoscale convective systems (MCSs) are organized convective precipitation regions that produce severe weather hazards, including heavy rainfall, flooding, and strong winds. Objective identification and tracking remain essential for MCS statistical analysis and operational forecasting. Conventional threshold-based methods using radar reflectivity or satellite brightness temperature are limited by terrain blockage, detection range constraints, and poor performance under nighttime or low-cloud conditions. This study developed a deep learning framework for MCS identification and tracking using hourly multi-source merged precipitation products from 18 summer seasons (2008–2025). A semi-supervised labeling strategy combining threshold-based pre-annotation and manual quality control was used to construct the dataset. Four architectures (DNN, U-Net, Attention U-Net, and Swin-Unet) were evaluated for pixel-level semantic segmentation, coupled with a conventional area overlap tracker and a proposed hybrid-criterion tracking method. Swin-Unet achieved the highest identification accuracy, with an Intersection over Union (IoU) of 0.90, and outperformed the other models in critical success index (CSI) and probability of detection (POD). The hybrid-criterion tracker, integrating area overlap, centroid distance, and deep feature similarity, attained a tracking accuracy of 0.94, with a 7.2% improvement over the area overlap method and a 34% lower break rate. A representative Meiyu rainfall event confirmed that the proposed approach captures the complete MCS life cycle. These results demonstrate that merged precipitation products offer a viable data source for operational MCS monitoring, particularly in radar-sparse regions.