Detecting hail-prone environments using a W-Net convolutional neural network

Hailstorms are known to cause great damage to agriculture and infrastructure. However, understanding and identifying the conditions favorable to hail remains a major challenge due to the complex, multi-scale nature of deep convective processes. In this study, we investigate the use of a W-Net convolutional neural network (CNN), which proved successful in image segmentation tasks, to identify atmospheric environments susceptible to hail from high-resolution numerical weather prediction data. The NOAA High-resolution Rapid Refresh (HRRR) model explicitly resolves convective processes owing to its fine spatial (3 km) and temporal (hourly) resolution. We consider meteorological variables from HRRR outputs relevant to deep convection and hail as input features for the W-Net model. Together with hail reports across the United States for the past ten years, we construct a deep learning framework. The trained network learns spatial patterns associated with hail-prone environments and produces gridded probability maps of hail occurrence. This data-driven approach shows the potential of deep learning methods for identification of hazardous convective weather. Once trained, the model can be applied to other regions, provided that the sub-daily, high-resolution meteorological fields are available.