Articles | Volume 23, issue 22
Research article
24 Nov 2023
Research article |  | 24 Nov 2023

Machine-learning-based investigation of the variables affecting summertime lightning occurrence over the Southern Great Plains

Siyu Shan, Dale Allen, Zhanqing Li, Kenneth Pickering, and Jeff Lapierre


Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2023-1020', Anonymous Referee #1, 16 Jun 2023
    • AC1: 'Reply on RC1', Siyu Shan, 04 Sep 2023
  • RC2: 'Comment on egusphere-2023-1020', Anonymous Referee #2, 05 Jul 2023
    • AC2: 'Reply on RC2', Siyu Shan, 04 Sep 2023

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Siyu Shan on behalf of the Authors (27 Sep 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (27 Sep 2023) by Peer Nowack
RR by Anonymous Referee #1 (29 Sep 2023)
RR by Anonymous Referee #2 (09 Oct 2023)
ED: Publish subject to technical corrections (09 Oct 2023) by Peer Nowack
AR by Siyu Shan on behalf of the Authors (16 Oct 2023)  Author's response   Manuscript 
Short summary

Several machine learning models are applied to identify important variables affecting lightning occurrence in the vicinity of the Southern Great Plains ARM site during the summer months of 2012–2020. We find that the random forest model is the best predictor among common classifiers. We rank variables in terms of their effectiveness in nowcasting ENTLN lightning and identify geometric cloud thickness, rain rate and convective available potential energy (CAPE) as the most effective predictors.

Final-revised paper