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.

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.

This paper considers a homogeneous multiple regression (HMR) model and a non-homogeneous multiple regression model, that is, ensemble model output statistics (EMOS), which are easy to implement postprocessing techniques to calibrate probabilistic forecasts that take the form of Gaussian probability density functions for continuous weather variables. The HMR and EMOS predictive means are biascorrected weighted averages of the ensemble member forecasts and the EMOS predictive variance is a linear function of the ensemble variance. We also consider the alternative implementations of HMR and EMOS which constrains the coefficients to be non-negative and we call these techniques as HMR+ and EMOS+, respectively. These techniques are applied to the forecasts of surface temperature over Pyeongchang area using 24-member Ensemble Prediction System for Global (EPSG). The performances are evaluated by rank histogram, residual quantile-quantile plot, means absolute error, root mean square error and continuous ranked probability score (CRPS). The results showed that HMR+ and EMOS+ models perform better than the raw ensemble mean, HMR and EMOS models. In the comparison of HMR+ and EMOS+ models, HMR+ performs slightly better than EMOS+ model in terms of CRPS, however they had a very similar CRPS and if there exists a ensemble spread-skill relationship, it is seen that EMOS is slightly better calibrated than the homogeneous multiple regression model.

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.

고체 재생연료인 SRF(Solid Recycled Fuel)는 다양한 폐기물로부터 제조된 연료로 평가받고 있다. 본 연구에서 사용된 SRF는 돈분(pig excrement)으로부터 제조되었다. 돈분을 호기성 소화조에서 발효시켜 바이오가스(메탄)를 추출하여 가스연료로 사용하고, 거의 액상상태의 잔유물중 침전물질을 고액분리과정을 거쳐, 얻어진 고체를 톱밥과 같은 가연성 유기물질과 혼합한 후 건조하여 제조한다. 본 연구 목적은 돈분으로부터 제조된 SRF가 열생산 보일러에서 혼소용 연료로 사용될 경우, 보일러의 연소 효율, 보일러의 성능유지 가능성 및 배출된 연소배가스의 배출특성을 파악하고자 하였다. SRF의 연소 및 배가스 배출특성을 파악하기 위하여 사용된 보일러는 10MWth 규모 순환유동층발전보일러가 사용되었다. 사용된 순환유동층연소로는 층 면적(bed area)이 1.92m², 연소로 높이는 약 13.0m이고, 연료투입구는 두 곳으로 분배기로부터 0.9m에 있다. CFBC에서 유연탄과 SRF의 혼소율은 5%이었으며, 연소로의 층 온도와 유동층 높이를 변화시켜, SRF 혼소에 따른 연소로의 성능유지, 연소효율, 배출가스의 배출특성을 고찰하였다. 본 연구에서 연소효율은 석탄만을 연소할 때 그리고 혼소할 때 큰 차이를 보이지 않아, SRF 5% 혼소에서 연소효율에 미치는 영향은 거의 미미하였다. Fig. 1에 나타낸 바와 같이 층높이 변화 실험에서 유동층높이가 600mmH₂O 이하일 때 층(bed)온도는 일정하게 유지할 수 있었으나, 연소로 전반적인 온도분포는 불안정한 상태를 유지하였다. 그러나 층 높이 600mmH₂O 이상에서는 보일러 전체적으로 매우 안정적인 온도유지가 가능하였다. 한편 연소배가스 배출특성을 살펴보면 NOx의 배출은 거의 유사한 조건으로 배출되었으며, SOx의 경우 석탄만을 연소할 경우보다 약 15% 증가하여 배출되는 경향을 보였다. 결과적으로 우리가 사용한 SRF는 순환유동층연소로에서 석탄과 5% 혼소할 경우, 석탄만을 연소할 경우와 대비하여 연소효율, 연소로 성능유지 및 연소배가스 배출특성에 큰 영향을 미치지 않으므로 고체재생연료로서 무리가 없다는 것으로 사료된다.