Articles | Volume 16, issue 12
https://doi.org/10.5194/gmd-16-3611-2023
https://doi.org/10.5194/gmd-16-3611-2023
Development and technical paper
 | 
30 Jun 2023
Development and technical paper |  | 30 Jun 2023

Convective-gust nowcasting based on radar reflectivity and a deep learning algorithm

Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao

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Latest update: 24 Feb 2024
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Short summary
Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG nowcasting has remained unattainable. Here, we developed a deep learning model — namely CGsNet — for 0—2 h of quantitative CG nowcasting, first achieving minute—kilometer-level forecasts. Based on the CGsNet model, the average surface wind speed (ASWS) and peak wind gust speed (PWGS) predictions are obtained. Experiments indicate that CGsNet exhibits higher accuracy than the traditional method.