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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]

This paper studied a snow event over North China on 21 February 2017, using aircraft in-situ data, a Lagrangian analysis tool, and WRF simulations with different microphysical schemes to investigate the supercooled layer of warm conveyor belts (WCBs). Based on the aircraft data, we found a fine vertical structure within clouds in the WCB and highlighted a 1–2 km thin supercooled liquid water layer with a maximum Liquid Water Content (LWC) exceeding 0.5 g kg−1 during the vertical aircraft observation. Although the main features of thermodynamic profiles were essentially captured by both modeling schemes, the microphysical quantities exhibited large diversity with different microphysics schemes. The conventional Morrison two-moment scheme showed remarkable agreement with in-situ observations, both in terms of the thermodynamic structure and the supercooled liquid water layer. However, the microphysical structure of the WCB clouds, in terms of LWC and IWC, was not apparent in HUJI fast bin scheme. To reduce such uncertainty, future work may focus on improving the representation of microphysics in bin schemes with in-situ data and using similar assumptions for all schemes to isolate the impact of physics. [ABSTRACT FROM AUTHOR]

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