Investigating the association between late spring Gulf of Mexico sea surface temperatures and U.S. Gulf Coast precipitation extremes with focus on Hurricane Harvey

Russell, Brook T.; Risser, Mark D.; Smith, Richard L.; Kunkel, Kenneth E.

doi:10.1002/env.2595

Cited by 10 publications

(8 citation statements)

References 37 publications

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“…As alluded to in the introduction, a wide variety of Bayesian and frequentist approaches, involving different types of approximations or simplifications, have been proposed to deal with high-dimensional spatio-temporal data, often under the assumption of data being exactly Gaussian. These include low-rank approaches (Cressie and Johannesson, 2008), the predictive process (Banerjee et al, 2008), covariance tapering (Furrer et al, 2006;Anderes et al, 2013), multi-resolution models (Nychka et al, 2015;Katzfuss, 2017), hierarchical nearest-neighbor Gaussian processes (Datta et al, 2016), the Vecchia approximation (Vecchia, 1988;Stein et al, 2004;Katzfuss and Guinness, 2019), the integrated nested Laplace approximation (Rue et al, 2009;Bakka et al, 2018), or more recently a frequentist approach for data modeled by a generalized-extreme value (GEV) distribution (Risser et al, 2019;Russell et al, 2020). See Heaton et al (2019) for a recent review and comparison of some of these methods.…”

Section: Predictions Of Meteorological Variables On a Latticementioning

confidence: 99%

Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models

et al. 2021

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With modern high-dimensional data, complex statistical models are necessary, requiring computationally feasible inference schemes. We introduce Max-and-Smooth, an approximate Bayesian inference scheme for a flexible class of latent Gaussian models (LGMs) where one or more of the likelihood parameters are modeled by latent additive Gaussian processes. Our proposed inference scheme is a two-step approach. In the first step (Max), the likelihood function is approximated by a Gaussian density with mean and covariance equal to either (a) the maximum likelihood estimate and the inverse observed information, respectively, or (b) the mean and covariance of the normalized likelihood function. In the second step (Smooth), the latent parameters and hyperparameters are inferred and smoothed with the approximated likelihood function. The proposed method ensures that the uncertainty from the first step is correctly propagated to the second step. Because the prior density for the latent parameters is assumed to be Gaussian and the approximated likelihood function is Gaussian, the approximate posterior density of the latent parameters (conditional on the hyperparameters) is also Gaussian, thus facilitating efficient posterior inference in high dimensions. Furthermore, the approximate marginal posterior distribution of the hyperparameters is tractable, and as a result, the hyperparameters can be sampled independently of the latent parameters. We show that the computational cost of Max-and-Smooth is close to being insensitive to the number of independent data replicates, and that it scales well with increased dimension of the latent parameter vector provided that its Gaussian prior density is specified with a sparse precision matrix. In the case of a large number of independent data replicates, sparse precision matrices, and high-dimensional latent vectors, the speedup is substantial in comparison to an MCMC scheme that infers the posterior density from the exact likelihood function. The accuracy of the Gaussian approximation to the likelihood function increases with the number of data replicates per latent model parameter. The proposed inference scheme is demonstrated on one spatially referenced real dataset and on simulated data mimicking spatial, temporal, and spatio-temporal inference problems. Our results show that Max-and-Smooth is accurate and fast.

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Section: Predictions Of Meteorological Variables On a Latticementioning

confidence: 99%

Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models

et al. 2021

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“…Extreme value theory (EVT) provides a probabilistic framework for performing statistical inference on the far upper tail of distributions, and is therefore useful in a wide variety of environmental applications. Examples include modeling extreme temperatures (Huang, Stein, McInerney, & Moyer, 2016; O'Sullivan, Sweeney, & Parnell, 2020; Stein, 2020a, 2020b), precipitation extremes (Fix, Cooley, & Thibaud, 2020; Hazra, Reich, & Staicu, 2020; Huang, Nychka, & Zhang, 2019; Russell, Risser, Smith, & Kunkel, 2020), and extremes in hydrology (Beck, Genest, Jalbert, & Mailhot, 2020; Towe, Tawn, Lamb, & Sherlock, 2019).…”

Section: Introductionmentioning

confidence: 99%

Modeling short‐ranged dependence in block extrema with application to polar temperature data

Russell

Huang

2020

Environmetrics

Self Cite

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The block maxima approach is an important method in univariate extreme value analysis. While assuming that block maxima are independent results in straightforward analysis, the resulting inferences maybe invalid when a series of block maxima exhibits dependence. We propose a model, based on a first‐order Markov assumption, that incorporates dependence between successive block maxima through the use of a bivariate logistic dependence structure while maintaining generalized extreme value (GEV) marginal distributions. Modeling dependence in this manner allows us to better estimate extreme quantiles when block maxima exhibit short‐ranged dependence. We demonstrate via a simulation study that our first‐order Markov GEV model performs well when successive block maxima are dependent, while still being reasonably robust when maxima are independent. We apply our method to two polar annual minimum air temperature data sets that exhibit short‐ranged dependence structures, and find that the proposed model yields modified estimates of high quantiles.

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“…Over the past couple of decades, EVT-based statistical methods have been widely used in climate studies to estimate extremes (i.e., the upper or lower tail distribution) of a single climate variable. In such analyses, one fits a generalized extreme value (GEV) distribution or generalized Pareto (GP) distribution to block maxima or threshold exceedances respectively to infer the so-called r-year return level (e.g., Zwiers and Kharin, 1998;Palutikof et al, 1999;Kharin and Zwiers, 2005;Jagger and Elsner, 2006;Cooley et al, 2007;Cooley and Sain, 2010;Kharin et al, 2013;Westra et al, 2013;Wang et al, 2016;Risser and Wehner, 2017;Huang et al, 2019b;Russell et al, 2019;Zhu et al, 2019). One main advantage of these EVT-based methods is that, in addition to estimating the exceedance probability of a given "large" value, one can quantitatively characterize the entire tail distribution (i.e., estimate the exceedance probability for any given "large" value and high quantiles).…”

Section: Introductionmentioning

confidence: 99%

Estimating Concurrent Climate Extremes: A Conditional Approach

Huang¹,

Monahan²,

Zwiers³

2020

Preprint

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Simultaneous concurrence of extreme values across multiple climate variables can result in large societal and environmental impacts. Therefore, there is growing interest in understanding these concurrent extremes. One way to approach this problem is to study the distribution of one climate variable given that another is extreme. In this work we develop a statistical framework for estimating bivariate concurrent extremes via a conditional approach, where univariate extreme value modeling is combined with dependence modeling of the conditional tail distribution using techniques from quantile regression and extreme value analysis to quantify concurrent extremes. We focus on the distribution of daily wind speed conditioned on daily precipitation taking its seasonal maximum. The Canadian Regional Climate Model large ensemble is used to assess the performance of the proposed framework both via a simulation study with specified dependence structure and via an analysis of the climate model-simulated dependence structure.

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Investigating the association between late spring Gulf of Mexico sea surface temperatures and U.S. Gulf Coast precipitation extremes with focus on Hurricane Harvey

Cited by 10 publications

References 37 publications

Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models

Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models

Modeling short‐ranged dependence in block extrema with application to polar temperature data

Estimating Concurrent Climate Extremes: A Conditional Approach

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