Browse > Article
http://dx.doi.org/10.5351/KJAS.2009.22.2.425

Bayesian Spatial Modeling of Precipitation Data  

Heo, Tae-Young (Department of Data Information, Korea Maritime University)
Park, Man-Sik (Department of Biostatistics, Korea University)
Publication Information
The Korean Journal of Applied Statistics / v.22, no.2, 2009 , pp. 425-433 More about this Journal
Abstract
Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.
Keywords
Precipitation; Bayesian kriging; Markov Chain Monte Carlo;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Handcock, M. S. and Stein, M. L. (1993). A Bayesian analysis of kriging, Technometrics, 35, 403-410   DOI   ScienceOn
2 Handcock, M. S. and Wallis, J. R. (1994). An approach to statistical spatio-temporal modeling of meteorological fields, Journal of the American Statistical Association, 89, 368-378   DOI   ScienceOn
3 Le, N. D. and Zidek, J. V. (1992). Interpolation with uncertain spatial covariance: A Bayesian alternative to kriging, Journal of Multivariate Analysis, 43, 351-374   DOI
4 Park, M. S. and Heo, T. Y. (2008). Seasonal spatial-temporal model for rainfall data of South Korea, Journal of Applied Sciences Research, accepted
5 R Development Core Team. (2008). R: A Language and Environment for Statistical Computing, R Foundation for Statistical Computing. Vienna, Austria, ISBN 3-900051-07-0
6 Svensson, C. and Rakhecha, P. R. (1998). Estimation of probable maximum precipitation for dams in the Hongru river catchment, China, Theoretical and Applied Climatology, 59, 79-91   DOI   ScienceOn
7 Bailey, R. G. (1998). Ecoregions: The Ecosystem Geography of the Oceans and Continents, Springer, New York
8 Banerjee,S., Carlin, B. P. and Gelfand, A. E. (2004). Hierarchical Modeling and Analysis for Spatial Data, Chapman & Hall/CRC, Florida
9 Brown, P. J., Le, N. D. and Zidek, J. V. (1994). Multivariate spatial interpolation and exposure to air pollutants, Canadian Journal of Statistics, 22, 489-509   DOI   ScienceOn
10 Carlin, B. P. and Louis, T. A. (2000). Bayes and Empirical Bayes Methods for Data Analysis, 2nd Edition, Chapman & Hall/CRC, Boca Raton
11 Cressie, N. A. C. (1993). Statistics for Spatial Data, John Wily & Sons, New York
12 De Oliveira, V., Kedern, B. and Short, D. A. (1997). Bayesian prediction of transformed Gaussian random fields, Journal of the American Statistical Association, 92, 1422-1433   DOI   ScienceOn
13 Diggle, P. J., Tawn, J. A. and Moyeed, R. A. (1998). Model-based geostatistics (with discussion), Applied Statistics, 47, 299-326
14 Finley, A. O., Banerjee, S. and Carlin, B. P. (2008). spBayes: Univariate and Multivariate Spatial Modeling, R package version 0.1-0
15 Ecker, M. D. and Gelfand, A. E. (1997). Bayesian variogram modeling for an isotropic spatial process, Journal of Agricultural, Biological and Environmental Statistics, 2, 347-369   DOI   ScienceOn
16 Eorn, J. K., Park, M.S., Heo, T. Y. and Huntsinger, L. F. (2006). Improving the prediction of annual average daily traffic for non-freeway facilities by applying spatial statistical method, Transportation Research Record, 1968, 20-29   DOI