Abstract and subjects
Hail risk assessment is crucial for businesses, particularly in the agricultural and insurance sectors, as it helps estimate and mitigate potential losses. Although significant attention has been given to short-term hail forecasting, the lack of research on climatological-scale hail risk estimation adds to the overall complexity of this task. Hail events are rare and localized, making their prediction a long-term open challenge.
One approach to address this challenge is to develop a model that classifies vertical profiles of meteorological variables as favorable for hail formation while neglecting important spatial and temporal information. The main advantages of this approach lie in its computational efficiency and scalability. A more advanced strategy involves combining convolutional layers and recurrent neural network blocks to process geospatial and temporal data, respectively.
This study compares the effectiveness of these two approaches and introduces a model suitable for forecasting changes in hail frequency.