The global capacity for energy generation by wind and solar technologies is currently around 650GW. This capacity is projected to double by 2020 making renewables a major component of the global energy landscape. Many renewable energy sources are particularly vulnerable to variations in weather and climate, such as generation by wind and solar technologies. There is therefore a need for accurate and reliable weather and climate information across a range of timescales from hours to years, so that energy companies can be better informed in their planning and decision-making. This includes their ability to account for the effects of weather and climate on energy supply/demand and on scheduling equipment maintenance. However, the current uptake of operational seasonal predictions by energy suppliers is generally low because of a perceived lack of skill and difficulty in interpreting forecast information, which both limit their usefulness.
While the ability to predict the weather up to a week ahead has improved steadily over the past few decades, predicting conditions for the forthcoming season has remained a major scientific challenge. However, there have been recent significant advances in predicting some of the major drivers of seasonal weather and climate variability in North America and Europe, such as the winter North Atlantic Oscillation (NAO) and the El Niño Southern Oscillation (ENSO). These advances in predictive skill have the potential to be translated into provision of more useful information for end-users in the renewable energy sector.
This PhD project will investigate how recent advances in seasonal prediction can be exploited to provide actionable information (e.g. on wind intensities) to end-users in the renewable energy sector. This will be achieved through strong engagement with CASE partner WEMC throughout the project. The focus will be on energy generation in North America and Europe, since these are regions where there have been advances in prediction capability and where there is substantial wind and solar energy capacity.