Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures

Simpson, Emma S.; Wadsworth, Jennifer L.

doi:10.1016/j.spasta.2020.100482

Cited by 26 publications

(25 citation statements)

References 30 publications

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“…As α quickly goes to zero with distance, all extremal dependence is instead exhibited through β. This is atypical of fits of this model for other applications (Wadsworth and Tawn, 2019, Shooter et al, 2020, Simpson and Wadsworth, 2021, where the α function drives the extremal behaviour of the considered processes, e.g., temperature and sea wave heights. Having β controlling extremal dependence would suggest that the process that generates the extreme precipitation we are modelling is somewhat rough; this concurs with the observed fields containing an extreme value shown in Figure 3, and consistent with the spatial nature of strong convective rainfall.…”

Section: Dependence Modelmentioning

confidence: 80%

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Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods

Richards¹,

Tawn²,

Brown³

2021

Preprint

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Inference on the extremal behaviour of spatial aggregates of precipitation is important for quantifying river flood risk. There are two classes of previous approach, with one failing to ensure self-consistency in inference across different regions of aggregation and the other requiring highly inflexible marginal and spatial dependence structure assumptions. To overcome these issues, we propose a model for high-resolution precipitation data, from which we can simulate realistic fields and explore the behaviour of spatial aggregates.Recent developments in spatial extremes literature have seen promising progress with spatial extensions of the Heffernan and Tawn ( 2004) model for conditional multivariate extremes, which can handle a wide range of dependence structures. Our contribution is twofold: new parametric forms for the dependence parameters of this model; and a novel framework for deriving aggregates addressing edge effects and sub-regions without rain.We apply our modelling approach to gridded East-Anglia, UK precipitation data. Returnlevel curves for spatial aggregates over different regions of various sizes are estimated and

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Section: Dependence Modelmentioning

confidence: 80%

“…(2020), Simpson and Wadsworth (2021), Simpson et al (2020), Huser and Wadsworth (2020). These papers cover extremal modelling of air and sea temperature fields and spatial wave heights.…”

Section: Introductionmentioning

confidence: 99%

Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods

Richards¹,

Tawn²,

Brown³

2021

Preprint

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“…Finally, the proposed model is fit to observations while neglecting spatial and temporal dependence. Modeling multidimensional extremes (as would be needed to explicitly account for spatial and/or temporal dependence) is an on-going challenge for the community (Huser and Wadsworth, 2020), where various techniques have been proposed, relying on hierarchical models (Gaetan and Grigoletto, 2007), copulas (Lee and Joe, 2018;Krupskii and Genton, 2021), and conditional modeling (Wadsworth and Tawn, 2019;Simpson and Wadsworth, 2021;Huang et al, 2021). Most of these methods ignore the bulk of the data or only focus on a single tail.…”

Section: Discussionmentioning

confidence: 99%

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

Krock¹,

Bessac²,

Stein³

et al. 2021

Preprint

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In traditional extreme value analysis, the bulk of the data is ignored, and only the tails of the distribution are used for inference. Extreme observations are specified as values that exceed a threshold or as maximum values over distinct blocks of time, and subsequent estimation procedures are motivated by asymptotic theory for extremes of random processes. For environmental data, nonstationary behavior in the bulk of the distribution, such as seasonality or climate change, will also be observed in the tails.To accurately model such nonstationarity, it seems natural to use the entire dataset rather than just the most extreme values. It is also common to observe different types of nonstationarity in each tail of a distribution. Most work on extremes only focuses on one tail of a distribution, but for temperature, both tails are of interest. This paper builds on a recently proposed parametric model for the entire probability distribution that has flexible behavior in both tails. We apply an extension of this model to historical records of daily mean temperature at several locations across the United States with different climates and local conditions. We highlight the ability of the method to quantify changes in the bulk and tails across the year over the past decades and under different geographic and climatic conditions. The proposed model shows good performance when compared to several benchmark models that are typically used in extreme value analysis of temperature.

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“…The HT model has been cited over 600 times, and is applied e.g. in oceanography (Ross et al, 2020), finance (Hilal et al, 2011), and spatio-temporal extremes (Simpson and Wadsworth, 2021). The HT model is a limit model and its form is motivated by derived limiting forms from numerous theoretical examples.…”

Section: Heffernan-tawn Modelmentioning

confidence: 99%

Extremal Characteristics of Conditional Models

Tendijck¹,

Tawn²,

Jonathan³

2022

Preprint

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Conditionally specified models are often used to describe complex multivariate data. Such models assume implicit structures on the extremes. So far, no methodology exists for calculating extremal characteristics of conditional models since the copula and marginals are not expressed in closed forms. We consider bivariate conditional models that specify the distribution of X and the distribution of Y conditional on X. We provide tools to quantify implicit assumptions on the extremes of this class of models. In particular, these tools allow us to approximate the distribution of the tail of Y and the coefficient of asymptotic independence η in closed forms. We apply these methods to a widely used conditional model for wave height and wave period. Moreover, we introduce a new condition on the parameter space for the conditional extremes model of Heffernan and Tawn (2004), and prove that the conditional extremes model does not capture η, when η < 1.

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Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures

Cited by 26 publications

References 30 publications

Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods

Modelling Extremes of Spatial Aggregates of Precipitation using Conditional Methods

Nonstationary seasonal model for daily mean temperature distribution bridging bulk and tails

Extremal Characteristics of Conditional Models

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