Rights statement: This is the peer reviewed version of the following article: Shooter, R, Ross, E, Tawn, J, Jonathan, P. On spatial conditional extremes for ocean storm severity. Environmetrics. 2019;e2562. https://doi.org/10.1002/env.2562 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/env.2562 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
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Final published version
Licence: CC BY: Creative Commons Attribution 4.0 International License
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
}
TY - JOUR
T1 - On spatial conditional extremes for ocean storm severity
AU - Shooter, Robert
AU - Tawn, Jonathan Angus
AU - Jonathan, Philip
AU - Ross, Emma
N1 - This is the peer reviewed version of the following article: Shooter, R, Ross, E, Tawn, J, Jonathan, P. On spatial conditional extremes for ocean storm severity. Environmetrics. 2019;e2562. https://doi.org/10.1002/env.2562 which has been published in final form at https://onlinelibrary.wiley.com/doi/full/10.1002/env.2562 This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.
PY - 2019/9/1
Y1 - 2019/9/1
N2 - We describe a model for the conditional dependence of a spatial process measured at one or more remote locations given extreme values of the process at a conditioning location, motivated by the conditional extremes methodology of Heffernan and Tawn. Compared to alternative descriptions in terms of max‐stable spatial processes, the model is advantageous because it is conceptually straightforward and admits different forms of extremal dependence (including asymptotic dependence and asymptotic independence). We use the model within a Bayesian framework to estimate the extremal dependence of ocean storm severity (quantified using significant wave height, HS) for locations on spatial transects with approximate east–west (E‐W) and north–south (N‐S) orientations in the northern North Sea (NNS) and central North Sea (CNS). For HS on the standard Laplace marginal scale, the conditional extremes “linear slope” parameter α decays approximately exponentially with distance for all transects. Furthermore, the decay of mean dependence with distance is found to be faster in CNS than NNS. The persistence of mean dependence is greatest for the E‐W transect in NNS, potentially because this transect is approximately aligned with the direction of propagation of the most severe storms in the region.
AB - We describe a model for the conditional dependence of a spatial process measured at one or more remote locations given extreme values of the process at a conditioning location, motivated by the conditional extremes methodology of Heffernan and Tawn. Compared to alternative descriptions in terms of max‐stable spatial processes, the model is advantageous because it is conceptually straightforward and admits different forms of extremal dependence (including asymptotic dependence and asymptotic independence). We use the model within a Bayesian framework to estimate the extremal dependence of ocean storm severity (quantified using significant wave height, HS) for locations on spatial transects with approximate east–west (E‐W) and north–south (N‐S) orientations in the northern North Sea (NNS) and central North Sea (CNS). For HS on the standard Laplace marginal scale, the conditional extremes “linear slope” parameter α decays approximately exponentially with distance for all transects. Furthermore, the decay of mean dependence with distance is found to be faster in CNS than NNS. The persistence of mean dependence is greatest for the E‐W transect in NNS, potentially because this transect is approximately aligned with the direction of propagation of the most severe storms in the region.
KW - conditional extremes
KW - nonstationary
KW - significant wave height
KW - spatial dependence
U2 - 10.1002/env.2562
DO - 10.1002/env.2562
M3 - Journal article
VL - 30
JO - Environmetrics
JF - Environmetrics
SN - 1180-4009
IS - 6
M1 - e2562
ER -