Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning – ADDENDUM. (28th February 2023)

Record Type:
Journal Article
Title:
Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning – ADDENDUM. (28th February 2023)
Main Title:
Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning – ADDENDUM
Authors:
Wu, Z.
Beucler, T.
Székely, E.
Ball, W.T.
Domeisen, D.I.V.
Abstract:
Is Part Of:
Environmental data science. Volume 2(2022)
Journal:
Environmental data science
Issue:
Volume 2(2022)
Issue Display:
Volume 2, Issue 2022 (2022)
Year:
2022
Volume:
2
Issue:
2022
Issue Sort Value:
2022-0002-2022-0000
Page Start:
Page End:
Publication Date:
2023-02-28
Subjects:
Explainable neural networks -- feature selection -- polar vortex extreme events -- prediction -- stratospheric variation
Environmental sciences -- Data processing -- Periodicals
577.0285
Journal URLs:
https://www.cambridge.org/core/journals/environmental-data-science/volume/76453F8B7082C69522D7F6E51D2DE865 ↗
DOI:
10.1017/eds.2023.3 ↗
Languages:
English
ISSNs:
2634-4602
Deposit Type:
Legaldeposit
View Content:
Available online (eLD content is only available in our Reading Rooms) ↗
Physical Locations:
British Library HMNTS - ELD Digital store
Ingest File:
26917.xml