Weather and Climate Dynamics
Weather and Climate Dynamics
WCD
Articles | Volume 4, issue 2
Articles | Volume 4, issue 2
https://doi.org/10.5194/wcd-4-287-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/wcd-4-287-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 4, issue 2
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

Viewed

Total article views: 3,683 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
2,723 883 77 3,683 80 119
  • HTML: 2,723
  • PDF: 883
  • XML: 77
  • Total: 3,683
  • BibTeX: 80
  • EndNote: 119
Views and downloads (calculated since 06 Oct 2022)
Cumulative views and downloads (calculated since 06 Oct 2022)

Viewed (geographical distribution)

Total article views: 3,683 (including HTML, PDF, and XML) Thereof 3,616 with geography defined and 67 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 06 Dec 2025
Download
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 %.
Read more
Share