Challenges in the selection of atmospheric circulation patterns for the wind energy sector

Abstract Atmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user-oriented classification of atmospheric circulation patterns in the Euro-Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K-means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year-to-year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro-Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.This research has been funded by the S2S4E (GA 776787) Horizon 2020 project, the Ministerio de Ciencia, Innovación y Universidades as part of the CLINSA project (CGL2017‐85791‐R) and the Juan de la Cierva – Incorporación Grant (IJCI‐2016‐29776). The analyses and plots of this work have been performed with the s2dverification (Manubens et al., 2018), CSTools (https://CRAN.R-project.org/package=CSTools) and startR (https://CRAN.R-project.org/package=startR) R‐language‐based software packages. Finally, we would like to thank Pierre‐Antoine Bretonnière, Margarida Samsó, Nicolau Manubens and Núria Pérez‐Zanón for their technical support at different stages of this project. We also acknowledge the two anonymous reviewers for their useful comments.Peer ReviewedPostprint (published version