Hail Intelligent Recognition Algorithm Based on HAM-Unet

Abstract

Hail, recognized as a severe convective weather phenomenon, carries significant destructive. Accurate identification is crucial to minimize economic damages and safeguard lives. The primary challenges in detecting hail include the scarcity of valid hail samples and the imbalance of these samples in high-resolution datasets. In response, this paper introduces the HAM Unet model, an hail identification framework that leverages multisource data and environmental factors. The model combines the FEM-Unet semantic segmentation architecture data fusion techniques. By integrating radar reflectivity, FY-4B satellite imagery, ERA5 climatic parameters, and topographical data, HAM-Unet improves both its precision and resilience. Extensive training and validation have equipped HAM-Unet with good capabilities, achieving remarkable scores in Probability of Detection (POD), False Alarm Rate (FAR), and the Critical Success Index (CSI). The model not only show potential in improving the accuracy and reliability of hail identification but also provides innovative ideas and methods for improvement of hail monitoring and warning Systems.