Open AccessA perfect prognosis downscaling methodology for seasonal prediction of local-scale wind speeds
Author(s) -
Jaume Cantallops Ramón,
Llorenç Lledó,
Pierre-Antoine Bretonnière,
Margarida Samsó,
Francisco J. DoblasReyes
Publication year - 2021
Publication title -
environmental research letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.37
H-Index - 124
ISSN - 1748-9326
DOI - 10.1088/1748-9326/abe491
Subject(s) - downscaling , climatology , environmental science , wind speed , teleconnection , meteorology , scale (ratio) , climate model , computer science , climate change , geology , geography , precipitation , oceanography , el niño southern oscillation , cartography
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.