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
Abstract
Subseasonal rainfall forecast skill is critical to support preparedness for hydrometeorological extremes. We assess how a process-informed evaluation, which subsamples forecasting model members based on their ability to represent potential predictors of rainfall, can improve monthly rainfall forecasts within Central America in the following month, using Costa Rica and Guatemala as test cases. We generate a constrained ensemble mean by subsampling 130 members from five dynamic forecasting models in the C3S multimodel ensemble based on their representation of both (a) zonal wind direction and (b) Pacific and Atlantic sea surface temperatures (SSTs), at the time of initialization. Our results show in multiple months and locations increased mean squared error skill by 0.4 and improved detection rates of rainfall extremes. This method is transferrable to other regions driven by slowly-changing processes. Process-informed subsampling is successful because it identifies members that fail to represent the entire rainfall distribution when wind/SST error increases.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 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: |
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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): | oai:eprints.whiterose.ac.uk:207324 |