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 |