한국수자원학회:학술대회논문집 (Proceedings of the Korea Water Resources Association Conference)
- 한국수자원학회 2009년도 학술발표회 초록집
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- Pages.1147-1151
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- 2009
Hierarchical Bayesian Model을 이용한 GCMs 의 최적 Multi-Model Ensemble 모형 구축
Optimal Multi-Model Ensemble Model Development Using Hierarchical Bayesian Model Based
- Kwon, Hyun-Han ;
- Min, Young-Mi (APEC Climate Center) ;
- Hameed, Saji N. (APEC Climate Center)
- 발행 : 2009.05.21
초록
In this study, we address the problem of producing probability forecasts of summer seasonal rainfall, on the basis of Hindcast experiments from a ensemble of GCMs(cwb, gcps, gdaps, metri, msc_gem, msc_gm2, msc_gm3, msc_sef and ncep). An advanced Hierarchical Bayesian weighting scheme is developed and used to combine nine GCMs seasonal hindcast ensembles. Hindcast period is 23 years from 1981 to 2003. The simplest approach for combining GCM forecasts is to weight each model equally, and this approach is referred to as pooled ensemble. This study proposes a more complex approach which weights the models spatially and seasonally based on past model performance for rainfall. The Bayesian approach to multi-model combination of GCMs determines the relative weights of each GCM with climatology as the prior. The weights are chosen to maximize the likelihood score of the posterior probabilities. The individual GCM ensembles, simple poolings of three and six models, and the optimally combined multimodel ensemble are compared.