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

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

doi:https://doi.org/10.5194/gmd-16-3611-2023

Articles | Volume 16, issue 12

https://doi.org/10.5194/gmd-16-3611-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

Special issue:

Fusion of radar polarimetry and numerical atmospheric modelling...

https://doi.org/10.5194/gmd-16-3611-2023

© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.

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

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Final revised paper (published on 30 Jun 2023)
Supplement to the final revised paper
Preprint (discussion started on 09 Jan 2023)

Interactive discussion

Status: closed

RC1: 'Comment on gmd-2022-272', Anonymous Referee #1, 16 Feb 2023

Dear editor,
I greatly appreciate the efficient and professional processing by your team. Please find attached the comments.
Thank you!

Best Regards

Citation: https://doi.org/10.5194/gmd-2022-272-RC1
RC2: 'Comment on gmd-2022-272', Anonymous Referee #2, 24 Feb 2023

See attached file (major comments followed by inline comments).

Citation: https://doi.org/10.5194/gmd-2022-272-RC2
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

Citation: https://doi.org/10.5194/gmd-2022-272-AC1

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.

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

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Peer review completion

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