In this paper, we used a nonhomogeneous Gaussian regression model (NGR) as the postprocessing techniques to calibrate probabilistic forecasts that take the form of probability density functions for temperature. We also performed the alternative implementation techniques of NGR, which are stationspecific ensemble model output statistics (EMOS) model. These techniques were applied to forecast temperature over Pyeongchang area using 24-member Ensemble Prediction System for Global (EPSG). The results showed that the station-specific EMOS model performed better than the raw ensemble and EMOS model.

Abstract
  1. 서론
  2. 자료
  3. 연구방법
  4. 연구결과
  5. 결론
  References

• 장동호(공주대학교 지리학과, Department of Geography, Kongju National University) | Dong Ho Jang
• 김찬수(공주대학교 응용수학과, Department of Applied Mathematics, Kongju National University) | Chansoo Kim Correspondence