Bayesian Model Averaging for Wind Speed Ensemble Forecasts Using Wind Speed and Direction

AbstractIn this paper, probabilistic wind speed forecasts are constructed based on ensemble numerical weather prediction (NWP) forecasts for both wind speed and wind direction. Including other NWP variables than the one subject to forecasting is common for statistical calibration of deterministic forecasts. However, this practice is rarely seen for ensemble forecasts, probably due to a lack of methods. A Bayesian modeling approach (BMA) is taken, and a flexible model class based on splines is introduced for the mean model. The spline model allows both wind speed and wind direction to be included nonlinearly. The proposed methodology is tested for forecasting hourly maximum 10-minute wind speeds based on ensemble forecasts from the European Center for Medium Range Weather Forecast at 204 locations in Norway for lead times from +12 to +108 hours. An improvement in the continuous ranked probability score is seen for approximately 85 % of the locations using the proposed method compared to standard BMA based ...