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A comparison study of Bayesian ensemble model output statistics for seasonal forcasts of precipitation
Journal of the Korean Data & Information Science Society 2019;30:385-99
Published online March 31, 2019;  https://doi.org/10.7465/jkdi.2019.30.2.385
© 2019 Korean Data and Information Science Society.

Seongil Jo1 · Sangin Lee2

1Department of Statistics (Institute of Applied Statistics), Chonbuk National University, 2Department of Information and Statistics, Chungnam National University
Correspondence to: Assistant Professor, Department of Information and Statistics, Deajeon 34134, Korea. E-mail: sanginlee44@gmail.com
Research of Seongil Jo was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF- 2017R1D1A3B03035235). Research of Sangin Lee was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1C1B2010113).
Received January 11, 2019; Revised January 23, 2019; Accepted January 23, 2019.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
This paper studies statistical post-processing methods for climate forecasts. In particular, we describe Bayesian linear regression model and Bayesian model averaging method which are the most popular methods, and explain a Bayesian nonparametric model using a Dirichlet process prior as an alternative of ensemble model output statistics. Based on three Bayesian ensemble model output statistics methods, the posterior distributions are derived and the posterior inferences are performed via Markov chain Monte Carlo methods. We compare three Bayesian ensemble model output statistics methods using leave-one-out cross-validation with precipitation data over Korean peninsula. The results show that the Bayesian ensemble model output statistics methods perform better than the general circulation model.
Keywords : Bayesian linear model, Bayesian model averaging, ensemble model output statistics, linear dependent Dirichlet process model, precipitation forecasts.