On the Application of Principal Component Analysis for Accurate Statistical-dynamical Downscaling of Wind Fields

A new methodology for the accurate statistical-dynamical downscaling of surface wind fields for long- term periods is described in this work. This new method is based on stratified sampling of long-term mean Sea Level Pressure fields combined with Principal Component Analysis for the determination of the most representative synthetic year. Validation is performed with 9 years of dynamically downscaled wind fields for the Iberian Peninsula obtained with the mesoscale model SKIRON. The results show that compared to the traditional method of dynamically downscaling random annual periods, the error in the predicted average wind speed is reduced by almost 30%.