Articles | Volume 4, issue 2
https://doi.org/10.5194/wcd-4-287-2023
https://doi.org/10.5194/wcd-4-287-2023
Research article
 | 
04 Apr 2023
Research article |  | 04 Apr 2023

Improved extended-range prediction of persistent stratospheric perturbations using machine learning

Raphaël de Fondeville, Zheng Wu, Enikő Székely, Guillaume Obozinski, and Daniela I. V. Domeisen

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on wcd-2022-55', Anonymous Referee #1, 08 Nov 2022
  • RC2: 'Comment on wcd-2022-55', Anonymous Referee #2, 23 Nov 2022
    • RC3: 'Added Comment by RC2', Anonymous Referee #2, 25 Nov 2022
  • AC1: 'ACs - wcd-2022-55', Raphael de Fondeville, 19 Dec 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision | EF: Editorial file upload
AR by Raphael de Fondeville on behalf of the Authors (21 Dec 2022)  Author's response   Author's tracked changes   Manuscript 
ED: Referee Nomination & Report Request started (29 Dec 2022) by Pedram Hassanzadeh
RR by Anonymous Referee #1 (21 Jan 2023)
RR by Anonymous Referee #2 (08 Feb 2023)
ED: Publish subject to minor revisions (review by editor) (09 Feb 2023) by Pedram Hassanzadeh
AR by Raphael de Fondeville on behalf of the Authors (16 Feb 2023)  Author's response   Author's tracked changes   Manuscript 
ED: Publish as is (04 Mar 2023) by Pedram Hassanzadeh
AR by Raphael de Fondeville on behalf of the Authors (10 Mar 2023)
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Short summary
We propose a fully data-driven, interpretable, and computationally scalable framework to characterize sudden stratospheric warmings (SSWs), extract statistically significant precursors, and produce machine learning (ML) forecasts. By successfully leveraging the long-lasting impact of SSWs, the ML predictions outperform sub-seasonal numerical forecasts for lead times beyond 25 d. Post-processing numerical predictions using their ML counterparts yields a performance increase of up to 20 %.