Abstract
This work provides a new methodology based on a statistical downscaling with a perfect prognosis approach to produce seasonal predictions of near-surface wind speeds at the local scale. Hybrid predictions combine a dynamical prediction of the four main Euro-Atlantic Teleconnections (EATC) and a multilinear statistical regression, which is fitted with observations and includes the EATC as predictors. Once generated, the skill of the hybrid predictions is assessed at 17 tall tower locations in Europe targeting the winter season. For comparative purposes, hybrid predictions have also been produced and assessed at a pan-European scale, using the ERA5 100 m wind speed as the observational reference. Overall, results indicate that hybrid predictions outperform the dynamical predictions of near-surface wind speeds, obtained from five prediction systems available through the Climate Data Store of the Copernicus Climate Change Service. The performance of a multi-system ensemble prediction has also been assessed. In all cases, the enhancement is particularly noted in northern Europe. By being more capable of anticipating local wind speed conditions in higher quality, hybrid predictions will boost the application of seasonal predictions outside the field of pure climate research.
Highlights
Recent advances in the fields of climate modelling and seasonal prediction have resulted in skilful seasonal predictions of surface variables over the extratropics (Merryfield et al 2020)
Once the dynamical forecasts of the predictors are generated, they are used in a statistical model that accounts for variations in wind speed related to variations in the EuroAtlantic Teleconnection (EATC) indices
For the purposes of our work, the PP approach represents an advantage over MOS, because (1) it uses one single statistical relationship that can be applied over various dynamical prediction systems, and (2) the amount of data available for fitting the relationship is not limited to the length of the hindcast, but to the timespan of the observational series (Marzban et al 2006)
Summary
Recent advances in the fields of climate modelling and seasonal prediction have resulted in skilful seasonal predictions of surface variables over the extratropics (Merryfield et al 2020). The renewable energy industry can profit from seasonal predictions of surface wind speed (Clark et al 2017, Torralba et al 2017) and wind power generation (Lledó et al 2019) to anticipate revenues, balance electricity supply and demand or schedule maintenance activities among others. Those predictions still suffer from some limitations, mainly due to (1) the limited skill levels on surface variables available from current seasonal prediction systems and (2) its relatively coarse spatial scales. These differences in magnitude are especially relevant for deriving indicators that are non-linear and sensitive to absolute magnitudes, such as the capacity factor (CF) of wind power (Pickering et al 2020)
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