Promoting New Links Between Energy and Meteorology

Troccoli, Alberto; Audinet, Pierre; Bonelli, P.; Boulahya, Mohammed Sadeck; Buontempo, Carlo; Coppin, P. A.; Dubus, Laurent; Dutton, John A.; Ebinger, Jane; Griggs, David; Gryning, Sven‐Erik; Gunasekera, Don; Harrison, Mike T.; Haupt, Sue Ellen; Lee, Trevor; Mailier, Pascal; Mathieu, Pierre-Philippe; Schaeffer, Roberto; Schroedter-Homscheidt, Marion; Zhu, Rong; Zillman, John W.

doi:10.1175/bams-d-12-00061.1

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Previous works have dealt with the sensitivity of the energy system to the variability at either short or long time scales, such as weather forecasts (Amin 2013, Foley et al 2012, Troccoli et al 2013, Vladislavleva et al 2013 or climate change projections (Ebinger and Vergara 2011, IPCC 2012, Koletsis et al 2016), while there are only a few very recent studies on the use of seasonal climate forecasts for wind energy applications (Clark et al 2017 andTorralba et al 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi-model seasonal forecasts for the wind energy sector

et al. 2019

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An assessment of the forecast quality of 10m wind speed by deterministic and probabilistic verification measures has been carried out using the original raw and two statistical bias-adjusted forecasts in global coupled seasonal climate prediction systems (ECMWF-S4, METFR-S3, METFR-S4 and METFR-S5) for boreal winter (December-February) season over a 22-year period 1991-2012. We follow the standard leave-one-out cross-validation method throughout the work while evaluating the hindcast skills. To minimize the systematic error and obtain more reliable and accurate predictions, the simple bias correction (SBC) which adjusts the systematic errors of model and calibration (Cal), known as the variance inflation technique, methods as the statistical post-processing techniques have been applied. We have also built a multi-model ensemble (MME) forecast assigning equal weights to datasets of each prediction system to further enhance the predictability of the seasonal forecasts. Two MME have been created, the MME4 with all the four prediction systems and MME2 with two better performing systems. Generally, the ECMWF-S4 shows better performance than other individual prediction systems and the MME predictions indicate consistently higher temporal correlation coefficient (TCC) and fair ranked probability skill score (FRPSS) than the individual models. The spatial distribution of significant skill in MME2 prediction is almost similar to that in MME4 prediction. In the aspect of reliability, it is found that the Cal method has more effective improvement than the SBC method. The MME4_Cal predictions are placed in close proximity to the perfect reliability line for both above and below normal categorical events over globe, as compared to the MME2_Cal predictions, due to the increase in ensemble size. To further compare the forecast performance for seasonal variation of wind speed, we have evaluated the skill of the only raw MME2 predictions for all seasons. As a result, we also find that winter season shows better performance than other seasons.

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Section: Introductionmentioning

confidence: 99%

Multi-model seasonal forecasts for the wind energy sector

et al. 2019

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“…the GFCS-Energy Exemplar, the World Economic Forum, the United Nations Framework Convention on Climate Change (Troccoli et al 2014), two special issues in international journals-one in Solar Energy following ICEM 2011 (Troccoli 2013) and one in MetZet following ICEM 2015 (Troccoli and SchroedterHomscheidt 2017)-and abundant discussions during ICEMs and at related events (e.g. Troccoli et al 2013), there is a wide-ranging set of issues that naturally feeds into the WEMC concept and plan of action. The plan of action includes (1) the organisation of a series of institutional workshop presentations, webinars and capacity building activities (including internships); (2) the assessments about the significance of future climate projections on energy resource and their implications for energy system investments; (3) the documentation on meteorological and energy data/metadata quality to assist the energy sector to easily access and make optimal use of these data; (4) the formulation and implementation of projects and programmes.…”

Section: Introduction To the World Energy And Meteorology Councilmentioning

confidence: 99%

Forging a Dialogue Between the Energy Industry and the Meteorological Community

Troccoli

Dubus

Haupt

et al. 2018

Weather &Amp; Climate Services for the Energy Industry

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The interplay between energy and meteorology (based on its broad meaning of weather, water and climate) has been steadily growing. For this relationship to continue flourishing, a formal structure for stakeholders to interact effectively is required. The process of formation of the World Energy & Meteorology Council (WEMC), an organisation aimed at promoting and strengthening such a relationship, is discussed in this chapter. Such a process involves building many diverse relationships, something which has been happening over several years, alongside the adoption of more formal practices such as stakeholder surveys. While the focus of this chapter is clearly on WEMC, this process could be used as a stimulus for analogous activities in the broader energy and meteorology area, specifically those at the national and regional levels, as well as similar activities straddling diverse disciplines, such as those promoted by the Global Framework for Climate Services (GFCS).

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Earth Observation in Support of the Energy Sector

Mathieu

2014

Weather Matters for Energy

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Promoting New Links Between Energy and Meteorology

Cited by 8 publications

References 5 publications

Multi-model seasonal forecasts for the wind energy sector

Multi-model seasonal forecasts for the wind energy sector

Forging a Dialogue Between the Energy Industry and the Meteorological Community

Earth Observation in Support of the Energy Sector

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