This paper used the Bayesian model averaging (BMA) with gamma distribution that takes the form of probability density functions to calibrate probabilistic forecasts of wind speed. We considered the alternative implementation of BMA, which was BMA gamma exchangeable model. This method was applied for forecasting of wind speed over Pyeongchang area using 51 members of the Ensemble Prediction System for Global (EPSG). The performances were evaluated by rank histogram, means absolute error, root mean square error, continuous ranked probability score and skill score. The results showed that BMA gamma exchangeable models performed better in forecasting wind speed, compared to the raw ensemble and ensemble mean.

• 한근희(공주대학교 응용수학과) | Keunhee Han
• 김찬식(공주대학교 응용수학과) | Chansik Kim
• 김찬수(공주대학교 응용수학과) | Chansoo Kim