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pp.49-67
In this study, we analyzed the performance of calibrated probabilistic forecasts of surface temperature over Pyeongchang area in Gangwon province by using Bayeisan Model Averaging (BMA). BMA has been proposed as a statistical post-processing method and a way of correcting bias and underdispersion in ensemble forecasts. The BMA technique provides probabilistic forecast that take the form of a weighted average of Gaussian predictive probability density function centered on the bias-corrected forecast for continuous weather variables. The results of BMA to calibrate surface temperature forecast from 24-member Ensemble Prediction System for Global (EPSG) are obtained and compared with those of multiple regression. The forecast performances such as reliability and accuracy are evaluated by Rank Histogram (RH), Residual Quantile-Quantile (R-Q-Q) plot, Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and the Continuous Ranked Probability Score (CRPS). The results showed that BMA improves the calibration of the equal weighted ensemble and deterministic-style BMA forecasts performs better than that of the deterministic forecast using the single best member.
