Process‐informed subsampling improves subseasonal rainfall forecasts in Central America

Kowal, K.M. orcid.org/0000-0002-9792-2540, Slater, L.J. orcid.org/0000-0001-9416-488X, Li, S. orcid.org/0000-0002-2479-8665 et al. (5 more authors) (2024) Process‐informed subsampling improves subseasonal rainfall forecasts in Central America. Geophysical Research Letters, 51 (1). e2023GL105891. ISSN 0094-8276

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Item Type: Article
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© 2024. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. https://creativecommons.org/licenses/by/4.0/

Keywords: rainfall; forecast; Central America; subseasonal; extreme weather; ensemble
Dates:
  • Published: 16 January 2024
  • Published (online): 5 January 2024
  • Accepted: 20 December 2023
  • Submitted: 11 August 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Social Sciences (Sheffield) > Department of Geography (Sheffield)
Depositing User: Symplectic Sheffield
Date Deposited: 08 Jan 2024 16:10
Last Modified: 08 Jan 2024 16:10
Status: Published
Publisher: American Geophysical Union (AGU)
Refereed: Yes
Identification Number: 10.1029/2023gl105891
Open Archives Initiative ID (OAI ID):

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