Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Leave-one-out Bayesian model averaging for probabilistic ensemble forecasting

  • Kim, Yongdai (Department of Statistics, Seoul National University) ;
  • Kim, Woosung (NAVER Corp.) ;
  • Ohn, Ilsang (Department of Statistics, Seoul National University) ;
  • Kim, Young-Oh (Department of Civil & Environmental Engineering, Seoul National University)
  • Received : 2016.10.10
  • Accepted : 2017.01.09
  • Published : 2017.01.31
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Abstract

Over the last few decades, ensemble forecasts based on global climate models have become an important part of climate forecast due to the ability to reduce uncertainty in prediction. Moreover in ensemble forecast, assessing the prediction uncertainty is as important as estimating the optimal weights, and this is achieved through a probabilistic forecast which is based on the predictive distribution of future climate. The Bayesian model averaging has received much attention as a tool of probabilistic forecasting due to its simplicity and superior prediction. In this paper, we propose a new Bayesian model averaging method for probabilistic ensemble forecasting. The proposed method combines a deterministic ensemble forecast based on a multivariate regression approach with Bayesian model averaging. We demonstrate that the proposed method is better in prediction than the standard Bayesian model averaging approach by analyzing monthly average precipitations and temperatures for ten cities in Korea.

Keywords

Acknowledgement

Supported by : Ministry of Land, Infrastructure and Transport

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