Abstract:

Based on the manual observation of hail records in Inner Mongolia, China, from 1959 to 2021, the spatial and temporal characteristics of hail distribution are analyzed, and a hail recognition method is constructed based on machine learning algorithms. The results are as follows: (1) Regarding temporal distribution, the number of hail days and affected stations in Inner Mongolia shows a decreasing trend. In terms of spatial distribution, hail events are predominantly concentrated in the Yinshan Mountains and the Greater Hinggan Mountains, with hail-prone areas extending along these mountain ranges. (2) Hail exhibits distinct seasonal and diurnal characteristics. The peak hail months in Inner Mongolia are from May to September, accounting for 91.79% of the annual hail days. The most frequent period for hail occurrences is between 12:00 BST and 19:00 BST. (3) Four machine learning algorithms (random forest, LightGBM, K-proximity, and decision tree) are used to model and evaluate hail events in Inner Mongolia through data preprocessing, predictor selection, model training, and tuning. Verification results indicate that machine learning methods effectively identify hail events, with the threat score of each model exceeding 0.83 and hit rates surpassing 92%. Among these, the random forest algorithm demonstrates the best recognition performance on the test set. These findings provide useful references for hail forecasting and artificial hail prevention in Inner Mongolia.

Key words: the number of hail station days, temporal and spatial characteristics, machine learning, hail identification