Empirical downscaling of wind speed probability distributions

Abstract

This paper presents a novel approach to developing empirically downscaled estimates of near‐surface wind speed and energy density and results from application of the technique to multiple stations in northern Europe. The downscaling takes a probabilistic approach in that it uses the mean and standard deviation of relative vorticity at 500 hPa and mean sea level pressure gradients computed using output from the ECHAM4/OPYC3 atmosphere‐ocean general circulation model as the predictors and parameters of the wind speed probability distribution at surface stations as the predictands. We demonstrate that this approach generates accurate depictions of the wind climate during the conditioning period and then apply the downscaling technique to examine changes between 1961–1990 and 2071–2100, which are compared to the results of dynamical downscaling. The empirically downscaled results for 1961–1990 and 2071–2100 show some evidence for small decreases in mean wind speed, 90th percentile wind speed, and energy density in 2071–2100 relative to 1961–1990. The projected changes are larger than the mean errors in the training period but smaller than current interannual variability. Rossby Centre regional climate model (RCM)–derived grid cell mean wind speeds exhibit a high degree of agreement with the empirically downscaled station wind speeds. However, in contrast to the empirical downscaling, simulations conducted using the Rossby Centre RCM indicate evidence for a small increase in the annual wind energy resource over northern Europe between the end of the 20th century and the end of the 21st century.