Wind Forecasting using HARMONIE with Bayes Model Averaging for Fine-Tuning

Abstract Wind-speed forecasts for a wind-farm in southwest Ireland were made for over one year using the operational HARMONIE mesoscale weather forecast model, and Bayes Model Averaging (BMA) for statistical post-processing to remove systematic local bias. The deterministic forecasts alone generated mean absolute errors of 1.7−2.0 ms −1 out to 24 hrs, when interpolated to the location of the met-mast. Application of BMA reduced these errors by about 15%, to 1.5−1.6 ms -1 , on average. Forecast errors do not degrade significantly as forecast lead-time increases, at least out to 24 hours.