Developing actionable seasonal climate information for the wind and solar energy industry

目前，全球风能和太阳能发电能力约为650千兆瓦。预计到2020年，这一能力将翻一番，使可再生能源成为全球能源格局的主要组成部分。许多可再生能源特别容易受到天气和气候变化的影响，例如风能和太阳能发电技术。因此，需要在从几小时到几年的一系列时间尺度上提供准确和可靠的天气和气候信息，以便能源公司在规划和决策时能够得到更好的信息。这包括他们考虑到天气和气候对能源供应/需求的影响以及安排设备维护的能力。然而，目前能源供应商对业务性季节性预报的接受率普遍较低，因为人们认为缺乏技能和难以解释预报信息，这两者都限制了它们的用途。尽管预测未来一周天气的能力在过去几十年里稳步提高，但即将到来的季节的预报条件仍然是一个重大的科学挑战。然而，最近在预测北美和欧洲季节性天气和气候变化的一些主要驱动因素方面取得了重大进展，例如冬季北大西洋涛动(NAO)和厄尔尼诺南方涛动(ENSO)。预测技术方面的这些进展有可能转化为为可再生能源部门的最终用户提供更有用的信息。该博士项目将研究如何利用季节性预测方面的最新进展，向可再生能源部门的最终用户提供可操作的信息(例如，关于风强的信息)。这将通过在整个项目中与CASE合作伙伴WEMC密切合作来实现。重点将放在北美和欧洲的能源发电上，因为这些地区的预测能力有所进步，风能和太阳能发电能力也很大。

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.