Abstract
In this paper, we apply three different Bayesian methods to the seasonal forecasting of the precipitation in a region around Korea (32.5°N–42.5°N, 122.5°E-132.5°E). We focus on the precipitation of summer season (June–July–August; JJA) for the period of 1979–2007 using the precipitation produced by the Global Data Assimilation and Prediction System (GDAPS) as predictors. Through cross-validation, we demonstrate improvement for seasonal forecast of precipitation in terms of root mean squared error (RMSE) and linear error in probability space score (LEPS). The proposed methods yield RMSE of 1.09 and LEPS of 0.31 between the predicted and observed precipitations, while the prediction using GDAPS output only produces RMSE of 1.20 and LEPS of 0.33 for CPC Merged Analyzed Precipitation (CMAP) data. For station-measured precipitation data, the RMSE and LEPS of the proposed Bayesian methods are 0.53 and 0.29, while GDAPS output is 0.66 and 0.33, respectively. The methods seem to capture the spatial pattern of the observed precipitation. The Bayesian paradigm incorporates the model uncertainty as an integral part of modeling in a natural way. We provide a probabilistic forecast integrating model uncertainty.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Baribieri, M. M., and J. O. Berger, 2004: Optimal predictive model selection. Ann. Stat., 32, 870–897.
Clyde, M., J. Ghosh, and M. Littman, 2011: Bayesian adaptive sampling for variable selection and model averaging. J. Comput. Graph. Statist., 20, 80–101.
Dellaportas, P., J. J. Forster, and I. Ntzoufras, 2002: On Bayesian model and variable selection using MCMC. Stat. Comp., 12, 27–36.
Drapper, D., 1995: Assessment of propagation of model uncertainty. J. Roy. Statist. Soc. Ser. B., 57, 45–98.
George, E. I., and R. E. McColloch, 1993: Variable selection via Gibbs sampling. J. Am. Stat. Assoc., 88, 881–889.
—, and —, 1997: Approaches for bayesian variable selection. Stat. Sini., 7, 339–374.
Green, P. J., 1995: Reversible jump markov chain monte carlo computation and bayesian model determination. Biometrika., 82, 711–732.
Kim, M.-K., I.-S. Kang, C.-K. Park, and K.-M. Kim, 2004: Superensemble prediction of regional precipitation over korea. Int. J. Climatol., 24, 777–790.
—, Y.-H. Kim, and W.-S. Lee, 2007: Seasonal prediction of Korean regional climate from preceding large-scale climate indices. Int. J. Climatol., 27, 925–934.
Luo, L., E. F. Wood, and M. Pan, 2007: Bayesian merging of multiple climate model forecasts for seasonal hydrological predictions. J. Geophys. Res., 112, doi:10.1029/2006JD007655.
O’Hara, R. B. and M. J. Sillanpää, 2009: A Review of Bayesian variable selection methods: what, how and which. Bayesian Analaysis, 4, 85–118.
Park, H., and S.-Y. Hong, 2007: An evaluation of a mass-flux cumulus parameterization scheme in the kma global forecast system. J. Meteor. Soc. Japan, 85, 151–169.
Raftery, A. E., D. Mafigan, and J. A. Hoeting, 1997: Bayesian model averaging for linear regression models. J. Am. Stat. Assoc., 92, 179–191.
Schwartz, G., 1978: Estimating the dimension of a model. Ann. Stat., 6, 461–464.
Xie, P. and P. A. Arkin, 1997: Global precipitation: A 17-year monthly analysis based on gauge observation, satellite estimates, and numerical model outputs. Bull. Amer. Meteor. Soc., 78, 2539–2588.
Zellner, A., 1986: On assessing prior distribution and bayesian regression analysis with g-prior distributions. In Bayesian Inference and Decision Technique: Essays in Honor of Bruno de Finetti, Amsterdam: North Holland, 233–243.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Jo, S., Lim, Y., Lee, J. et al. Bayesian regression model for seasonal forecast of precipitation over Korea. Asia-Pacific J Atmos Sci 48, 205–212 (2012). https://doi.org/10.1007/s13143-012-0021-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13143-012-0021-7