Hail disaster recognition method based on artificial intelligence with Doppler radar data

Hail weather is a strong weather phenomenon produced by a system of severe convective weather. Furthermore, hail weather has the characteristics of short duration, strong locality, and great destructive capacity and is a type of disastrous weather. Therefore, the recognition and tracking of hail weather have always been significant research topics in the field of meteorology. Doppler weather radar can obtain high precision and high frequency data by observing strong weather information with high temporal and spatial resolution. However, weather radar cannot directly recognize and inform forecasters of strong weather conditions; thus it takes considerable time and energy to identify and monitor the weather artificially. Based on the continuous development of artificial intelligence (AI) technology, combining the advantages of different observation data and using AI to process it are important ways to improve the strong weather monitoring and early warning systems. Our study presents a hail weather recognition method based on AI. In the proposed method, faster region-based convolutional neural network deep learning is applied to recognize hail weather areas, and the accuracy and precision on the test set are more than 86% and 97%, respectively. The reliability of the proposed method is verified by theoretical analysis, the actual hail situation, and comparison with the traditional hail recognition method. The experimental results provide a significant reference for the recognition and early warning of hail weather.