A new statistical approach to downscale wind speed distributions at a site in northern Europe

Abstract This paper explores a statistical regression approach to downscale large‐scale global circulation model output to the wind speed distribution at the hub‐height of tall wind turbines. The methodology is developed for Cabauw, using observational, ERA‐Interim and ECHAM5 data. The regression analysis is based on the parameters of the probability distribution functions (pdfs) and includes a variable evaluation prior to the development of the statistical models. During winter ECHAM5 performs very well in representing the ERA‐Interim wind speed pdf at hub‐height. However, during summer, the hub‐height wind speed pdf is not well represented by ECHAM5. A regression analysis shows that during summer‐day the hub‐height wind speed is strongly linked to the wind speed at higher, skillfully represented levels. The summer‐day hub‐height wind speed can therefore be skillfully predicted using wind speed pdf parameters of higher levels (R2 of the model using 500 m wind speed scale parameter as a predictor is 0.84). During the summer‐night, the stable boundary layer is much shallower and the statistical model shows that solely the higher level wind speed is not able to skillfully predict the hub‐height wind speed pdf (R2 of 0.59). Including temperature information in the downscaling model substantially improves the prediction of the summer‐night hub‐height wind speed pdf (R2 adjusted of 0.68).