Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1-4 over South Africa

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dc.contributor.author Phakula, Steven
dc.contributor.author Landman, Willem Adolf
dc.contributor.author Engelbrecht, Christina Johanna
dc.date.accessioned 2024-07-30T06:59:23Z
dc.date.available 2024-07-30T06:59:23Z
dc.date.issued 2024-01
dc.description DATA AVAILABILITY STATEMENT : The S2S model data used here are freely available through WWRP/WCRP S2S project portal (http://apps.ecmwf.int/datasets/data/s2s), and the ERA5 reanalysis data are freely available from the C3S (https://climate.copernicus.eu/climate-reanalysis). en_US
dc.description.abstract The probabilistic forecast skill level of statistically downscaled European Centre for Medium-Range Weather Forecasts (ECMWF) subseasonal-to-seasonal (S2S) forecasts is determined in predicting maximum and minimum temperatures for weeks 1–4 lead times during 20-year December–January–February (DJF) seasons from 2001 to 2020 over South Africa. Skilful S2S forecasts are vital in assisting decision-makers in the development of contingency planning for any eventualities that may arise because of weather and climate phenomena. Extreme high- and low-temperature events over a prolonged period can lead to hyperthermia and hypothermia, respectively, and can lead to loss of life. The results from the relative operating characteristic (ROC) and reliability diagrams indicate that the ECMWF S2S model has skill in predicting maximum temperature up to week 3 ahead, particularly over the central and eastern parts of South Africa. The ROC scores indicate that the model has skill in predicting minimum temperature up to week 4 ahead for the above-normal category, particularly over the central and eastern parts of South Africa. Reliability diagrams indicate that the model has a tendency of overestimating the below-normal category when predicting both maximum and minimum temperatures for weeks 1–4 lead times over South Africa. Furthermore, canonical correlation analysis (CCA) pattern analysis suggests that when there are anomalously positive and negative predicted 850-hPa geopotential heights located over South Africa, there are anomalously hot and cold conditions during the DJF seasons over most parts of South Africa, respectively. These results suggests that statistical downscaling of model forecasts can improve forecast skill. Moreover, the results suggest that there is potential for S2S predictions in South Africa, and as such, S2S prediction system for maximum and minimum temperatures can be developed. en_US
dc.description.department Geography, Geoinformatics and Meteorology en_US
dc.description.librarian hj2024 en_US
dc.description.sdg SDG-13:Climate action en_US
dc.description.sponsorship NRF GRANT Global Change Grand Challenge; Application of Knowledge for the Management of Extreme Climate Events (apecx); University of Pretoria; South African Weather Service. en_US
dc.description.uri http://wileyonlinelibrary.com/journal/met en_US
dc.identifier.citation Phakula, S., Landman, W. A., & Engelbrecht, C. J. (2024). Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1–4 over South Africa. Meteorological Applications, 31(1), e2176. https://doi.org/10.1002/met.2176. en_US
dc.identifier.issn 1350-4827 (print)
dc.identifier.issn 1469-8080 (online)
dc.identifier.other 10.1002/met.2176
dc.identifier.uri http://hdl.handle.net/2263/97306
dc.language.iso en en_US
dc.publisher Wiley en_US
dc.rights © 2024 The Authors. Meteorological Applications published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License. en_US
dc.subject Subseasonal-to-seasonal (S2S) en_US
dc.subject Canonical correlation analysis en_US
dc.subject Probabilistic skill metrics en_US
dc.subject South Africa (SA) en_US
dc.subject Subseasonal-to-seasonal predictions en_US
dc.subject SDG-13: Climate action en_US
dc.title Probabilistic skill of statistically downscaled ECMWF S2S forecasts of maximum and minimum temperatures for weeks 1-4 over South Africa en_US
dc.type Article en_US


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