the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm
Haixia Xiao
Yaqiang Wang
Yu Zheng
Yuanyuan Zheng
Xiaoran Zhuang
Hongyan Wang
Mei Gao
Abstract. Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CGs nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning – namely, CGsNet – is developed for 0–2 h of quantitative CGs nowcasting, first achieving minute-kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations, it can produce realistic and spatiotemporally consistent ASWS predictions in CGs events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01° × 0.01° and a 6-minute temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CGs nowcasting technology can be applied to provide real-time quantitative CGs forecasts and alerts the damaging wind events in meteorological services.
Haixia Xiao et al.
Status: closed
- RC1: 'Comment on gmd-2022-272', Anonymous Referee #1, 16 Feb 2023
- RC2: 'Comment on gmd-2022-272', Anonymous Referee #2, 24 Feb 2023
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AC1: 'Comment on gmd-2022-272', Haixia Xiao, 18 Apr 2023
Dear Editor,
Sorry for the late reply, it takes time to improve the paper and the experiment part, especially.
Thank you very much for handling our submission! We greatly appreciate the comments from the reviewers on our manuscript entitled “Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm” (GMD-2022-272). Those comments are all valuable and very helpful for revising and improving our paper. We have tried our best to carefully revise the manuscript according to these suggestions. Our responses (in blue) to each of the comments are attached to the “Supplement”.
Best regards,
Haixia Xiao
(on behalf of all co-authors)
Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210008, China
Status: closed
- 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
Dear Editor,
Sorry for the late reply, it takes time to improve the paper and the experiment part, especially.
Thank you very much for handling our submission! We greatly appreciate the comments from the reviewers on our manuscript entitled “Convective Gusts Nowcasting Based on Radar Reflectivity and a Deep Learning Algorithm” (GMD-2022-272). Those comments are all valuable and very helpful for revising and improving our paper. We have tried our best to carefully revise the manuscript according to these suggestions. Our responses (in blue) to each of the comments are attached to the “Supplement”.
Best regards,
Haixia Xiao
(on behalf of all co-authors)
Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210008, China
Haixia Xiao et al.
Data sets
Convective Gusts Datasets( radar reflectivity and wind observations) Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao https://doi.org/10.7910/DVN/PIZU7V
Model code and software
The code of CGsNet model Haixia Xiao, Yaqiang Wang, Yu Zheng, Yuanyuan Zheng, Xiaoran Zhuang, Hongyan Wang, and Mei Gao https://doi.org/10.7910/DVN/PIZU7V
Haixia Xiao et al.
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