Bayesian Spatial Modeling of Extreme Precipitation Return Levels

By Daniel Cooley, D. Nychka, Philippe Naveau. 2007

574 citations recorded by the source corpus.

Subfields: General Agricultural and Biological Sciences, Global and Planetary Change, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/6b54e350d8686abdeec236b04008a740b3409774

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In General Agricultural and Biological Sciences

In Global and Planetary Change

In Management Science and Operations Research

Citations in the corpus

Modelling Time Series Extremes (2012, background): “Examples are Coles & Casson (1998), Casson & Coles (1999), Fawcett & Walshaw (2006a), Cooley et al. (2006), Cooley et al. (2007) and Sang & Gelfand (2009).”
Extreme rainfall in West Africa: A regional modeling (2012, methodology): “Bayesian spatial modeling: this approach has been recently proposed to produce maps of precipitation extremes [Cooley et al., 2007; Cooley and Sain, 2010].”
Changes in concurrent monthly precipitation and temperature extremes (2013, methodology): “While extreme values of precipitation and temperature often are addressed independently by employing univariate statistical methods (Cooley et al 2007, Katz 2010, AghaKouchak and Nasrollahi 2010, Zhang et al 2011, AghaKouchak et al 2010), analyses of concurrence of climate extremes are scarce, and…”
Statistical modeling of hot spells and heat waves. (2010, methodology): “Cooley et al. (2007) ran simulations to show that using thresholds in the middle of the discretization interval provides numerically stable estimations, which is the approach we took here.”
INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles (2018, methodology): “In particular, Cooley et al. (2007) use Gaussian processes to capture spatial dependence and covariate-driven trends in precipitation data, taking advantage of simulation-based Markov chain Monte-Carlo (MCMC) methods for the estimation of posterior distributions.”
Generalized Additive Models for Exceedances of High Thresholds With an Application to Return Level Estimation for U.S. Wind Gusts (2019, methodology): “Existing approaches to capturing such variation range from parametric (Smith 1989) to stochastic (Casson and Coles 1999; Cooley, Nychka, and Naveau 2007).”
A Bayesian Hierarchical Approach to Multivariate Nonstationary Hydrologic Frequency Analysis (2018, methodology): “Finally, uncertainty is propagated naturally in a Bayesian model without the need for bootstrapping or other ad hoc uncertainty estimation methods (Cooley et al., 2007).”
On spatial extremes: With application to a rainfall problem (2008, background): “Recently, Cooley, Nychka and Naveau [9] presented a Bayesian framework for dependence in spatial extremes.”
A review of statistical methods to analyze extreme precipitation and temperature events in the Mediterranean region (2019, methodology): “Furthermore, the GPD-B method, used by Cooley et al. (2007), provides the greatest return level values for the majority of stations.”
A Bayesian hierarchical approach to regional frequency analysis (2011, background): “This framework builds on previous work by several authors [in particular, Wikle et al., 1998; Perreault, 2000; Micevski et al., 2006; Micevski, 2007; Cooley et al., 2007; Lima and Lall, 2009, 2010; Aryal et al., 2009], who explored the usefulness of spatial and temporal Bayesian hierarchical models.”
Bayesian hierarchical modeling of extreme hourly precipitation in Norway (2013, methodology): “As the quality of data improves, a peak-over-threshold approach, such as the GP model used in Cooley et al. (2007), could give more accurate estimation of local extremes.”
Bayesian Inference from Composite Likelihoods, with an Application to Spatial Extremes (2009, methodology): “…the hierarchical model with a conditional independence assumption in the data layer, yielding a product of K independent GEV densities, analogous to Cooley et al. [2007] or Sang and Gelfand [2009]; M2 the max-stable process hierarchical model with no adjustment; M3 the max-stable process…”
Geostatistics of Dependent and Asymptotically Independent Extremes (2013, methodology): “Related work is described by Cooley et al. (2012b). Extrapolation to estimate probabilities of”
Considering covariates in the covariance structure of spatial processes (2011, background): “…Copyright 2011 John Wiley & Sons, Ltd. Environmetrics 2011; 22: 487–500 4 9 0 assume the function d(x) maps the locations from G onto the larger space C. Cooley et al. (2007) use covariates (but not geographic coordinates) to model extreme precipitation.”
Geostatistics of extremes (2012, background): “The first of these employs underlying latent processes, conditional on which standard extremal models are applied (Coles & Casson 1998; Casson & Coles 1999; Cooley et al. 2006, 2007; Fawcett & Walshaw 2006; Sang & Gelfand 2009a).”