European Climate Services

Buontempo, Carlo

doi:10.1007/978-3-319-68418-5_3

Cited by 2 publications

(2 citation statements)

References 22 publications

(17 reference statements)

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“… Scaife et al, 2014 , Smith et al, 2016 ), have meant that seasonal forecasting climate services are now starting to be developed in earnest (e.g. Palin et al, 2016 , Viel et al, 2016 , Prudhomme et al, 2017 , Clark et al, 2017 , Buontempo, 2018 , Thornton et al, 2019 ). At the same time, the introduction of increasingly important levels of weather-dependent renewable electricity generation means that demand for skillful and reliable seasonal forecasting services, tailored to the requirements of users in the energy sector, is only likely to increase in the coming years.…”

Section: Introductionmentioning

confidence: 99%

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

et al. 2022

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Section: Introductionmentioning

confidence: 99%

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

et al. 2022

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“…Scaife et al, 2014;Smith et al, 2016), have meant that seasonal forecasting climate services are now starting to be developed in earnest (e.g. Palin et al, 2016;Viel et al, 2016;Prudhomme et al, 2017;Clark et al, 2017;Buontempo, 2018;Thornton et al, 2019). At the same time, the introduction of increasingly important levels of weather-dependent renewable electricity generation means that demand for skillful and reliable seasonal forecasting services, tailored to the requirements of users in the energy sector, is only likely to increase in the coming years.…”

Section: Introductionmentioning

confidence: 99%

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

Bett¹,

Thornton²,

Troccoli³

et al. 2019

Preprint

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We demonstrate the current levels of skill for seasonal forecasts of wind and irradiance in Europe, using forecast systems available from the Copernicus Climate Change Service (C3S). While skill is patchy, there is potential for the development of climate services for the energy sector. Following previous studies, we show that a simple linear regression-based method, using the hindcast and forecast ensemble means, provides a straightforward approach to produce reliable probabilistic seasonal forecasts in the cases where there is skill. This method extends naturally to using a larger-scale feature of the climate, such as the North Atlantic Oscillation, as the climate model predictor, providing opportunities to improve the skill in some cases.We further demonstrate that taking a seasonal average and a regional (e.g. national) average means that wind and solar power generation are highly correlated with single climate variables (wind speed and irradiance): the detailed non-linear transformations from meteorological variables to energy variables, which can be essential for precision on weather forecasting timescales and for climatological studies, are usually not necessary when producing seasonal forecasts of these average quantities.Together, our results demonstrate that, in the cases where there is skill in seasonal forecasts of wind speed and irradiance, or a correlated larger-scale climate predictor, it can be very straightforward to forecast seasonal mean wind and solar power generation based on those climate variables, without requiring complex transformations. This greatly simplifies the process of developing a useful seasonal climate service.

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European Climate Services

Cited by 2 publications

References 22 publications

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

A simplified seasonal forecasting strategy, applied to wind and solar power in Europe

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