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Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

While previous work on the climatology of Northern China has focused on mean wind speed, wind gusts have received comparatively less attention but are equally important to various users. In this paper, an observed hourly maximum gust wind speeds (HMGS) dataset across North China has been created by using time series from 174 meteorological stations. The dataset offers superior quality, high spatiotemporal near-surface HMGS series for North China spanning from 2015 to 2022. The objective of this study is first to improve our understanding of the spatiotemporal gusts climatology in North China by analyzing the observed gust data. Second, we aim to supplement the observational data by using gust analysis and forecast data with a high spatial–temporal resolution from model simulations. The spatial characteristics of the seasonal cycle of the simulated analysis of mean HMGS and the performance in predicting gusts based on the geographical locations and elevations of the validation stations were investigated by comparing it with the observations. Results indicate the following: 1) Wind direction and intensity are affected by the terrain and climate conditions of different weather stations. Stations situated along the Bohai Bay coastal region and at higher-elevation areas of North China exhibit a higher mean HMGS than those located in the coastal and inland plains. 2) The probability density function curves for wind speed and wind direction exhibit notable variations across different elevation intervals. The contribution of moderate and strong gust wind speeds increases gradually with increasing altitude, while the gust directions in mountainous areas exhibit relatively consistent patterns due to the increased exposure to synoptic-scale forcing at higher elevations. 3) The nowcasting prediction system analysis of mean HMGS provides a higher horizontal resolution that is capable of capturing the contrasts between land and sea, as well as the influence of high HMGS associated with large-scale circulations in high-elevation regions. Significance Statement: The purpose of this study is to better understand the spatiotemporal gust climatology in North China and the performance of the model-simulated gust analysis and forecast data. This is important because gusts conditions differ due to varying topographic and climatic conditions of different weather stations. Our results provide a valuable insight into the climatological variations of HMGS, their drivers, and identify the deficiencies in the model-simulation gusts. [ABSTRACT FROM AUTHOR]