A comparison study of extreme precipitation from six different regional climate models via spatial hierarchical modeling

DOI10.1007/s10687-009-0098-2zbMath1238.62138OpenAlexW2068106118MaRDI QIDQ549647

Daniel Cooley, Erin M. Schliep, Jennifer A. Hoeting, Stephan R. Sain

Publication date: 18 July 2011

Published in: Extremes (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10687-009-0098-2

zbMATH Keywords

generalized extreme value distributionNARCCAPhierarchical Bayes modelintrinsic autoregressive modelreanalysis-driven simulations

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to environmental and related topics (62P12) Bayesian inference (62F15) Statistics of extreme values; tail inference (62G32) Numerical analysis or methods applied to Markov chains (65C40)

Related Items (8)

Investigating precipitation extremes in South Carolina with focus on the state's October 2015 precipitation event ⋮ Extracting a common signal in tree ring widths with a semi-parametric Bayesian hierarchical model ⋮ Bayesian hierarchical modeling of extreme hourly precipitation in Norway ⋮ Computationally efficient spatial modeling of annual maximum 24‐h precipitation on a fine grid ⋮ Extreme value analysis for evaluating ozone control strategies ⋮ A spatial analysis of multivariate output from regional climate models ⋮ Improved return level estimation via a weighted likelihood, latent spatial extremes model ⋮ Toward Optimal Fingerprinting in Detection and Attribution of Changes in Climate Extremes

Uses Software

• GMRFLib
• spam
• spBayes

Cites Work

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• The pairwise beta distribution: A flexible parametric multivariate model for extremes
• Models for stationary max-stable random fields
• Geostatistical modelling for spatial interaction data with application to postal service performance
• Spatial regression models for extremes
• Spatial hierarchical modeling of precipitation extremes from a regional climate model
• Bayesian Spatial Modeling of Extreme Precipitation Return Levels
• Gaussian Markov Random Fields
• Dependence measures for extreme value analyses

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