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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]

Before 2008, the number of surface observation stations in China was small. Thus, the surface observation data were too sparse to effectively support the High-resolution China Meteorological Administration's Land Assimilation System (HRCLDAS) which ultimately inhibited the output of high-resolution and high-quality gridded products. This paper proposes a statistical downscaling model based on a deep learning algorithm in super-resolution to research the above problem. Specifically, we take temperature as an example. The model is used to downscale the 0.0625° × 0.0625°, 2-m temperature data from the China Meteorological Administration's Land Data Assimilation System (CLDAS) to 0.01° × 0.01°, named CLDASSD. We performed quality control on the paired data from CLDAS and HRCLDAS, using data from 2018 and 2019. CLDASSD was trained on the data from 31 March 2018 to 28 February 2019, and then tested with the remaining data. Finally, extensive experiments were conducted in the Beijing-Tianjin-Hebei region which features complex and diverse geomorphology. Taking the HRCLDAS product and surface observation data as the "true values" and comparing them with the results of bilinear interpolation, especially in complex terrain such as mountains, the root mean square error (RMSE) of the CLDASSD output can be reduced by approximately 0.1°C, and its structural similarity (SSIM) was approximately 0.2 higher. CLDASSD can estimate detailed textures, in terms of spatial distribution, with greater accuracy than bilinear interpolation and other sub-models and can perform the expected downscaling tasks. [ABSTRACT FROM AUTHOR]