Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Yaqiang Wang
Chinese Academy of Meteorological Sciences, Beijing 100081, China
Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Yuanyuan Zheng
Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Xiaoran Zhuang
Key Laboratory of Transportation Meteorology of China Meteorological Administration, Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041, China
Jiangsu Meteorological Observatory, Nanjing 210041, China
Hongyan Wang
Chinese Academy of Meteorological Sciences, Beijing 100081, China
Mei Gao
Chinese Academy of Meteorological Sciences, Beijing 100081, China
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Total article views: 1,994 (including HTML, PDF, and XML)
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Total article views: 2,874 (including HTML, PDF, and XML)
Thereof 2,797 with geography defined
and 77 with unknown origin.
Total article views: 1,994 (including HTML, PDF, and XML)
Thereof 1,948 with geography defined
and 46 with unknown origin.
Total article views: 880 (including HTML, PDF, and XML)
Thereof 849 with geography defined
and 31 with unknown origin.
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.
Due to the small-scale and nonstationary nature of convective wind gusts (CGs), reliable CG...
