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Challenges and prospects for numerical techniques in atmospheric modeling
File(s)
Author(s)
Li, J
Li, Y
Steppeler, J
Laurian, A
Fang, F
Type
Journal Article
Date Issued
2023-02-01
Date Acceptance
2023-02-01
Citation
Bulletin of the American Meteorological Society, 2023, 104 (2), pp.449-455
URI
URL
DOI
ISSN
0003-0007
Publisher
American Meteorological Society
Start Page
449
End Page
455
Journal / Book Title
Bulletin of the American Meteorological Society
Volume
104
Issue
2
Copyright Statement
© 2023 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).
Identifier
https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000936085500001&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=1ba7043ffcc86c417c072aa74d649202
Subjects
Adaptive models
Data assimilation
Deep learning
forecasting
Meteorology & Atmospheric Sciences
modeling
Neural networks
Numerical analysis
Numerical weather prediction
Physical Sciences
Science & Technology
Publication Status
Published
Date Publish Online
2023-02-16