동질성과 비 동질성 회귀모델을 이용한 평창지역 기온에 대한확률론적 예측
A Probabilistic Forecast of Temperature in Pyeongchang using Homogeneous and Nonhomogeneous Regression Models
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Abstract
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.
참고문헌 (16)
- Chandler, R. E., "On the use of generalized linear models for interpreting climate variability", Environmetrics, 16(7), pp.699~715 (2005)
- Glahn, H. R., "The use of model output statistics (MOS) in objective weather forecasting", Journal of Applied Meteorology, 11, pp.1203~1211 (1972)
- Gneiting, T., "Weather forecasting with ensemble methods", Science, 310, pp.248~249 (2005)
- Gneiting, T., "Strictly proper scoring rules, prediction, and estimation", Journal of the American Statistical Association, 102, pp.359~378 (2007)
- Gneiting, T., "Calibrated probabilistic forecasting using ensemble model output statistics and minimum CRPS estimation", Monthly Weather Review, 113, pp.1098~1118 (2005)
- Hamill, T. M., "Interpretation of rank histograms for verifying ensemble forecasts", Monthly Weather Review, 129, pp.550~560 (2001)
- Hamill, T. M., "Ensemble reforecasting: Improving mediumrange forecast skill using retrospective forecasts", Monthly Weather Review, 132, pp.1434~1447 (2004)
- Hersbach, H., "Decomposition of the continuous ranked probability score for ensemble prediction systems", Weather Forecasting, 15, pp.559~570 (2000)
- Horel, J., "Weather Support for the 2002Winter Olympic and Paralympic Games", Bulletin of the American Meterological Society, 83, pp.227~240 (2002)
- Mailhot, J., "Environment Canada’s Experimental Numerical Weather Prediction Systems for the Vancouver 2010 Winter Olympic and Paralympic Games", Bulletin of the American Meteorological Society, 91(8), pp.1073~1085 (2010)
- Marzban, C., "On the effect of correlations on rank histograms: reliability of temperature and wind speed forecasts from finescale ensemble reforecasts", Monthly Weather Review, 139, pp.295~310 (2011)
- Murphy, A. H., "What is good forecast? An essay on the nature of goodness in weather forecasting", Weather and Forecasting, 8, pp.281~293 (1993)
- Raftery, A. E., "Using Bayesian model averaging to calibrate forecast ensembles", Monthly Weather Review, 133, pp.1155~1174 (2005)
- Unger, D. A., "A method to estimate the continuous ranked probability score", Ninth Conference on Probability and Statistics in Atmospheric Sciences, pp.206~213 (1985), American Meteor
- Weigend, A. S., "Predicting daily probability distributions of S&P500 returns", Journal of Forecasting, 19, pp.375~392 (2000)
- Wilks, D. S., "Statistical Methods in the Atmospheric Sciences" (2006), Academic Press