A New Anticyclone Identification Method Based on Mask R-CNN Model and Its Application

In recent decades, frequent cold waves and low-temperature events in mid-to-high latitude Eurasia have severely impacted socioeconomic activities in Northeast China. Accurately identifying anticyclones is essential due to their close relation to cold air activity. This study proposes a new anticyclone identification method using the Mask region-based convolutional neural network (Mask R-CNN) model to detect synoptic-scale anticyclones by capturing their two-dimensional structural features and investigating their relationship with snow-ice disasters in Northeast China. It is found that compared with traditional objective identification methods, the new method better captures the overall structural characteristics of anticyclones, significantly improving the description of large-scale, strong anticyclones. Specifically, it incorporates 7.3% of small-scale anticyclones into larger-scale systems. Anticyclones are closely correlated with local cooling and cold air mass changes over Northeast China, with 60% of anticyclones accompanying regional cold air mass accumulation and temperature drops. Two case studies of the rare rain-snow and cold wave events revealed that these events were preceded by the generation and eastward expansion of an upstream anticyclone identified by the new method. This demonstrates that the proposed method can effectively track anticyclones and the evolution of cold high-pressure systems, providing insights into extreme cold events.