The rapid development of economy and culture in Beijing-Tianjin-Hebei region has a higher requirement for instantaneous strong wind forecasts. Correctly estimating and predicting instantaneous strong winds on the ground level in winter, especially accurate high-resolution grid-point forecasting of gusts under complex terrain condition, is of great significance for improving the service for major Winter Olympics events, the safe operation in the capital and surrounding cities, and disaster prevention and mitigation capabilities. This study establishes a relationship between the gust coefficient and wind speed, wind direction and terrain height based on long-term series of observation data in Beijing-Tianjin-Hebei. Combined with objective statistical analysis method, gust observation data fusion technology and grid point deviation correction technology, an objective gust forecast method is developed, which not only retains the model physical parameters and local climate characteristics, but also utilizes the grid point deviation correction technology. The results of batch verification and case analysis during the Winter Olympics show that the average absolute errors in the Zhangjiakou competition area and the Yanqing competition area are below 2.3 m/s and 3.0 m/s, respectively. The forecast score of gust wind speed above level 8 in the Yanqing competition area is above 0.5. It solves the bottleneck problem of large gust prediction errors and meets the on-site service requirements of major Winter Olympic activities.

Thunderstorm gusts are a common and hazardous type of severe convective weather, characterized by a small spatial scale, short duration, and significant destructive power. They often lead to severe disasters, highlighting the critical importance of their accurate forecasting. Previous studies have explored the environmental factors and spatiotemporal distribution characteristics of thunderstorm gusts, highlighting the need for improved forecasting methods. In recent years, artificial intelligence techniques have shown promise in enhancing the accuracy of thunderstorm gust forecasting, with various machine learning algorithms and models having been developed. This paper proposes a multiscale feature fusion module called Thunderstorm Gusts Block (TG-Block) and a deep learning model named Thunderstorm Gusts net (TG-net) based on the Attention U-net and TG-TransUnet models, and employs interpretable methods such as Integrated Gradient, Deep Learning Importance Features, and Shapley Additive exPlanations to validate the model’s practical relevance and reliability. The analysis of feature importance underscores the model’s ability to capture key thermodynamic and multiscale weather characteristic information for thunderstorm gust nowcasting. It is, however, worth emphasizing that these conclusions are only based on a limited number of thunderstorm gust examples, and the evaluation results may be affected by specific weather types and sample sizes. Nonetheless, TG-net has been put into real-time operation at the Institute of Urban Meteorology, and we will continue to rigorously validate its performance and make any necessary optimizations and enhancements based on feedback to ensure the robustness and stability of the model.