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Bayesian Spatial Modeling of Precipitation Data
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 Title & Authors
Bayesian Spatial Modeling of Precipitation Data
Heo, Tae-Young; Park, Man-Sik;
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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.
Precipitation;Bayesian kriging;Markov Chain Monte Carlo;
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