Sub-seasonal to seasonal forecasts through self-supervised learning (Proposals Track)

dc.contributor.author	Thümmel, Jannik
dc.contributor.author	Schlör, Jakob
dc.contributor.author	Butz, Martin V.
dc.contributor.author	Goswami, Bedartha
dc.date.accessioned	2024-07-19T08:07:23Z
dc.date.available	2024-07-19T08:07:23Z
dc.date.issued	2023
dc.identifier.uri	http://hdl.handle.net/10900/155193
dc.language.iso	en	de_DE
dc.publisher	Climate Change AI	de_DE
dc.relation.uri	https://www.climatechange.ai/papers/iclr2023/37	de_DE
dc.subject.ddc	004	de_DE
dc.title	Sub-seasonal to seasonal forecasts through self-supervised learning (Proposals Track)	de_DE
dc.type	Article	de_DE
dc.type	ConferenceObject	de_DE
utue.personen.roh	Thuemmel, Jannik
utue.personen.roh	Strnad, Felix
utue.personen.roh	Schlör, Jakob
utue.personen.roh	Butz, Martin V.
utue.personen.roh	Goswami, Bedartha
dcterms.isPartOf.ZSTitelID	ICLR 2023 Workshop on Tackling Climate Change with Machine Learning	de_DE