Spatial Hierarchical Modeling of Precipitation Extremes From a Regional Climate Model |
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Authors: | Daniel Cooley Stephan R Sain |
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Institution: | 1.Department of Statistics,Colorado State University,Fort Collins,USA;2.Geophysical Statistics Project, Institute for Mathematics Applied to Geosciences,National Center for Atmospheric Research,Boulder,USA |
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Abstract: | The goal of this work is to characterize the extreme precipitation simulated by a regional climate model (RCM) over its spatial
domain. For this purpose, we develop a Bayesian hierarchical model. Since extreme value analyses typically only use data considered
to be extreme, the hierarchical approach is particularly useful as it sensibly pools the limited data from neighboring locations.
We simultaneously model the data from both a control and future run of the RCM which allows for easy inference about projected
change. Additionally, this hierarchical model is the first to spatially model the shape parameter which characterizes the
nature of the distribution’s tail. Our hierarchical model shows that for the winter season, the RCM indicates a general increase
in 100-year precipitation return levels for most of the study region. For the summer season, the RCM surprisingly indicates
a significant decrease in the 100-year precipitation return level. |
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Keywords: | |
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