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

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Version: Final published version
License: CC BY 4.0
Serval ID
serval:BIB_0CE5C474601D
Type
Article: article from journal or magazin.
Collection
Publications
Institution
UNIL/CHUV
Title
Modeling stratospheric polar vortex variation and identifying vortex extremes using explainable machine learning
Journal
Environmental Data Science
Author(s)
Wu Zheng, Beucler Tom, Székely Enikő, Ball William T., Domeisen Daniela I.V.
ISSN
2634-4602
Publication state
Published
Issued date
2022
Volume
1
Language
english
URN
urn:nbn:ch:serval-BIB_0CE5C474601D3
OAI-PMH
oai:serval.unil.ch:BIB_0CE5C474601D
DOI
10.1017/eds.2022.19
Open Access
Yes
Funding(s)
Swiss National Science Foundation / PP00P2_170523
Swiss National Science Foundation / PP00P2_198896
Swiss National Science Foundation / PP00P2_170523
Swiss National Science Foundation / PP00P2_198896
Swiss National Science Foundation / PP00P2_170523
Swiss National Science Foundation / PP00P2_198896
Create date
19/01/2023 17:49
Last modification date
20/01/2023 7:08
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