Keane, R.J., Parker, D.J. orcid.org/0000-0003-2335-8198, Dunn-Sigouin, E. et al. (2 more authors) (2025) Mid-Latitude Versus Tropical Scales of Predictability and Their Implications for Forecasting. Meteorological Applications, 32 (4). e70055. ISSN: 1350-4827
Abstract
Weather predictability varies between tropical and middle latitudes: rotational effects enable forecasts on moderate spatial scales up to 10 days in middle latitudes, while longer term predictions are less reliable; in contrast, tropical weather is challenging to predict at short lead times, but seasonal forecasts are more accurate due to the influence of larger-scale oscillations, such as slowly varying oceanic surface conditions. This behaviour has been demonstrated in previous studies, but has yet to be focused on in detail, despite its importance to the development of forecasting systems in Tropical regions. This study systematically evaluates precipitation in weather prediction models across both regions using the fractions skill score, evaluating performance at progressively longer lead times and averaging scales, and compares the results with an evaluation based on upper air error kinetic energy. The results confirm that the prediction systems perform better on smaller scales and shorter lead times at middle latitudes and on larger scales and longer lead times at tropical latitudes. A “crossover” in performance is seen at forecast lead times of 5–7 days, a result that appears to be consistent across a range of model resolutions, and occurs both when specifically comparing European and African domains and when comparing whole latitude bands. This differential pattern of model skill even occurs for machine learning-based forecast models, suggesting that it is a fundamental property of the atmosphere rather than an effect of the construction of currently used operational forecasting systems. These findings highlight the need for different forecasting methodologies in tropical regions to address the lack of short-term predictability and leverage long-term statistical predictability.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2025 Crown copyright and The Author(s). This is an open access article under the terms of the Creative Commons Attribution License (CC-BY 4.0), which permits unrestricted use, distribution and reproduction in any medium, provided the original work is properly cited. This article is published with the permission of the Controller of HMSO and the King's Printer for Scotland. |
Keywords: | Africa, machine learning, predictability, tropical weather forecasting |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Environment (Leeds) > School of Earth and Environment (Leeds) |
Funding Information: | Funder Grant number NERC (Natural Environment Research Council) NE/P021077/1 Met Office No external Reference |
Depositing User: | Symplectic Publications |
Date Deposited: | 07 May 2025 10:18 |
Last Modified: | 20 Aug 2025 12:19 |
Status: | Published |
Publisher: | Wiley |
Identification Number: | 10.1002/met.70055 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:226102 |