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Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data
SMHI, Research Department, Meteorology.ORCID iD: 0000-0003-0524-6440
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2024 (English)In: Wind Energy Science, ISSN 2366-7443, E-ISSN 2366-7451, Vol. 9, no 4, p. 821-840Article in journal (Refereed) Published
Place, publisher, year, edition, pages
2024. Vol. 9, no 4, p. 821-840
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Meteorology and Atmospheric Sciences
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Climate; Meteorology
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URN: urn:nbn:se:smhi:diva-6603DOI: 10.5194/wes-9-821-2024ISI: 001198193200001OAI: oai:DiVA.org:smhi-6603DiVA, id: diva2:1851917
Available from: 2024-04-16 Created: 2024-04-16 Last updated: 2025-02-07Bibliographically approved

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Machine learning methods to improve spatial predictions of coastal wind speed profiles and low-level jets using single-level ERA5 data(5393 kB)94 downloads
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File name FULLTEXT01.pdfFile size 5393 kBChecksum SHA-512
a18e7daa08c49d5a065223edb26b13b1626ba7f067c97095dfe939d8df507813c1153757b0228944ac7c7ec4d8fbecc6e1334bd41e6d4abc017f530c946d6f6b
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Körnich, Heiner

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