Adaptive post-processing of short-term wind forecasts for energy applications

We present a new method of reducing the error in predicted wind speed, thus enabling better management of wind energy
facilities. A numerical weather prediction model, COSMO, was used to produce 48 h forecast data every day in 2008 at
horizontal resolutions of 10 and 3 km. A new adaptive statistical method was applied to the model output to improve the
forecast skill. The method applied corrective weights to a set of forecasts generated using several post-processing methods.
The weights were calculated based on the recent skill of the different forecasts. The resulting forecast data were compared
with observed data, and skill scores were calculated to allow comparison between different post-processing methods. The
total root mean square error performance of the composite forecast is superior to that of any of the individual methods.