Hostname: page-component-77c78cf97d-57qhb Total loading time: 0 Render date: 2026-04-25T05:02:17.549Z Has data issue: false hasContentIssue false
  • English
  • Français

Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning – ADDENDUM

Published online by Cambridge University Press:  28 February 2023

Z. Wu Open the ORCID record for Z. Wu [Opens in a new window] ,
T. Beucler ,
E. Székely ,
W.T. Ball  and
D.I.V. Domeisen

Abstract

An abstract is not available for this content. As you have access to this content, full HTML content is provided on this page. A PDF of this content is also available in through the ‘Save PDF’ action button.

Keywords

Explainable neural networksfeature selectionpolar vortex extreme eventspredictionstratospheric variation

Information

Type
Addendum
Information
Environmental Data Science , Volume 2 , 2023 , e8
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
© The Author(s), 2023. Published by Cambridge University Press

Cambridge University Press & Assessment regrets the omission of four ORCID iDs in the above article. The authors ORCID iDs are listed below:

Tom Beucler: 0000-0002-5731-1040

Eniko Székely: 0000-0001-5710-9814

William T. Ball: 0000-0002-1005-3670

Daniela I.V. Domeisen: 0000-0002-1463-929X

References

Wu, Z, Beucler, T, Székely, E, Ball, W, and Domeisen, D (2022) Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning. Environmental Data Science, 1, E17. doi:10.1017/eds.2022.19CrossRefGoogle Scholar