This study used a quantile regression model and a non-homogeneous regression model to calibrate probabilistic forecasts of wind speed. These techniques were applied to the forecasts of wind speed over Pyeongchang area using 51-member European Centre for Medium-Range Weather Forecast (ECMWF). Reliability analysis was carried out by using rank histogram to identify the statistical consistency of ensemble forecasts and corresponding observations. The performances were evaluated by rank histogram, mean absolute error, root mean square error and continuous ranked probability score. The results showed that the forecasts of quantile regression and non-homogeneous regression models performed better than the raw ensemble forecasts. However, the differences of prediction skills between quantile regression and nonhomogeneous regression models were insignificant.

This paper applied the ensemble model output statistics (EMOS) with truncated normal distribution, which are easy to implement postprocessing techniques, to calibrate probabilistic forecasts of wind speed that take the form of probability density functions. We also considered the alternative implementations of EMOS, which were EMOS exchangeable model and reduced EMOS model. These techniques were applied to the forecasts of wind speed over Pyeongchang area using 51 members of the Ensemble Prediction System for Global (EPSG). The performances were evaluated by rank histogram, mean absolute error, root mean square error and continuous ranked probability score. The results showed that EMOS models with truncated normal distribution performed better than the raw ensemble and ensemble mean. Especially, the reduced EMOS model exhibited better prediction skill than EMOS exchangeable model in most stations of study area.