The Skill Assessment of Seasonal Wind Prediction in South Africa - Parbhoo, Trisha (Department of Environmental and Geographical Science, 2022)
To assess the skill of seasonal wind prediction in South Africa, the period between 1983 and 2018 was studied, unless otherwise stated. A total of sixteen (16) sites were studied across South Africa to ensure that each region across the country was represented in the study. Seasonally, prediction of the wind energy resource is important in order to plan for local plant operations, such as downtime for maintenance during low wind periods. Additionally, if a forecast indicates a large wind energy resource for a particular season, maintenance at other non-wind energy generation plants may be planned. However, currently there is limited information about seasonal prediction of wind in South Africa and no information about the skill of such a forecast. This thesis begins the process of addressing this knowledge gap. Correlations between the CFS hindcast data and the reanalyses show the windspeed forecast is skilful over the southern regions of South Africa during December – January – February (austral summer), and over northern regions during June – July – August (austral winter), where the Pearson Correlation Coefficient ranges between ~0.5 and ~0.75. This skill is a function of the regional atmospheric stability during the respective seasons which are in turn a function of large-scale circulation features that govern synoptic processes driving the regional wind predictability. La Niña results in a weakening of the high pressure systems to the west of the country, which could cause lower wind speeds specifically over the north-western regions of South Africa. The deepening of an extended sub-tropical low pressure trough over the interior of South Africa during La Niña potentially causes an increase in the pressure gradient, resulting in higher wind speeds over the eastern, north-eastern and eastern interior regions. During La Niña events literature indicates that the south-easterly wind speeds over regions of the Western Cape increase, and this could be due to the southward movement of the South Atlantic High Pressure. During a negative SAM phase, the correlation between the SAM Index and the reanalyses indicates that there is an enhanced predictability in the eastern interior regions of the country, specifically during September – October – November(where the r-value is < -0.4 over specific regions), which is likely a function of the strong belt of westerly winds that move equatorward and therefore closer to South Africa. A low-pressure trough develops and extends across the central interior of country which results in an increased pressure gradient between the interior and the high pressure cells off the coasts of South Africa, resulting in increased wind speeds over the majority of the country. These results indicates that ENSO and SAM are drivers of wind predictability over South Africa at the seasonal scale. The CFS hindcast captures the ENSO forcing (El Niño, neutral and La Niña) of wind speeds for the majority of the same regions and seasons as found in the reanalysis data. However, the CFS does not capture the SAM forcing of seasonal wind speeds described above. Therefore the CFS forecast system is a useful system with respect to seasonal wind forecasts given the ENSO forcing is captured, however, this study recommends that model development research should focus on developing forecast systems that capture other large-scale drivers, apart from ENSO, such as the SAM. The study also demonstrates that it is essential to use multiple reanalyses if assessing the skill of the seasonal forecast using reanalysis products. Results show that there are statistically significant differences between the reanalysis wind datasets. The ERA5 wind speeds were lower in the majority of the sites (14 out of the 16 sites) in comparison to the ERA-Interim and CFSR wind speeds. This indicated that when only one reanalysis was used, it could artificially show higher or lower skill levels. Based on the results above, this study concludes that there is better predictability in the CFS when the atmosphere is stable, and in certain parts of the country under La Niña and negative SAM during specific times of the year; for example, ENSO has greater effect over South Africa during austral summer months. During these skilful periods, the seasonal prediction system can be used to inform seasonal wind energy potential across the country. Understanding when and where a wind forecast is skilful could assist in efficient energy supply planning, inform plant operations such as maintenance and contribute towards the shift to a low carbon economy.