Articles | Volume 16, issue 12
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


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2022-272', Anonymous Referee #1, 16 Feb 2023
  • RC2: 'Comment on gmd-2022-272', Anonymous Referee #2, 24 Feb 2023
  • AC1: 'Comment on gmd-2022-272', Haixia Xiao, 18 Apr 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Haixia Xiao on behalf of the Authors (18 Apr 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (20 Apr 2023) by Nicola Bodini
RR by Anonymous Referee #1 (08 May 2023)
ED: Publish as is (09 May 2023) by Nicola Bodini
AR by Haixia Xiao on behalf of the Authors (11 May 2023)  Author's response   Manuscript 
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.