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
Viewed
Total article views: 3,351 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
2,343
903
105
3,351
179
129
178
HTML: 2,343
PDF: 903
XML: 105
Total: 3,351
Supplement: 179
BibTeX: 129
EndNote: 178
Views and downloads (calculated since 09 Jan 2023)
Cumulative views and downloads
(calculated since 09 Jan 2023)
Total article views: 2,444 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
Supplement
BibTeX
EndNote
1,818
535
91
2,444
179
119
166
HTML: 1,818
PDF: 535
XML: 91
Total: 2,444
Supplement: 179
BibTeX: 119
EndNote: 166
Views and downloads (calculated since 30 Jun 2023)
Cumulative views and downloads
(calculated since 30 Jun 2023)
Total article views: 907 (including HTML, PDF, and XML)
HTML
PDF
XML
Total
BibTeX
EndNote
525
368
14
907
10
12
HTML: 525
PDF: 368
XML: 14
Total: 907
BibTeX: 10
EndNote: 12
Views and downloads (calculated since 09 Jan 2023)
Cumulative views and downloads
(calculated since 09 Jan 2023)
Viewed (geographical distribution)
Total article views: 3,351 (including HTML, PDF, and XML)
Thereof 3,280 with geography defined
and 71 with unknown origin.
Total article views: 2,444 (including HTML, PDF, and XML)
Thereof 2,398 with geography defined
and 46 with unknown origin.
Total article views: 907 (including HTML, PDF, and XML)
Thereof 882 with geography defined
and 25 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...
