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Consistent design criteria for south China sea with a large-scale extreme value model

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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Consistent design criteria for south China sea with a large-scale extreme value model. / Raghupathi, L.; Randell, D.; Jonathan, P. et al.

2016. 844-863 Paper presented at Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

Raghupathi, L, Randell, D, Jonathan, P & Ewans, KC 2016, 'Consistent design criteria for south China sea with a large-scale extreme value model', Paper presented at Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia, 25/03/14 - 28/03/14 pp. 844-863. https://doi.org/10.4043/26668-MS

APA

Raghupathi, L., Randell, D., Jonathan, P., & Ewans, K. C. (2016). Consistent design criteria for south China sea with a large-scale extreme value model. 844-863. Paper presented at Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia. https://doi.org/10.4043/26668-MS

Vancouver

Raghupathi L, Randell D, Jonathan P, Ewans KC. Consistent design criteria for south China sea with a large-scale extreme value model. 2016. Paper presented at Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia. doi: 10.4043/26668-MS

Author

Raghupathi, L. ; Randell, D. ; Jonathan, P. et al. / Consistent design criteria for south China sea with a large-scale extreme value model. Paper presented at Offshore Technology Conference-Asia, Kuala Lumpur, Malaysia.20 p.

Bibtex

@conference{c660df32f29f4cb1878580afcfb21446,
title = "Consistent design criteria for south China sea with a large-scale extreme value model",
abstract = "Existing metocean design criteria for offshore facilities in the South China Sea have been estimated using different data and procedures, some of which are at least partly ad hoc. As a result, it is probable that existing criteria are inconsistent, in the sense that assets designed to the same design codes have different realised levels of integrity. To address this concern in this paper, we apply a large-scale extreme value model adapted to parallel computing environment, applied to the recent high-resolution SEAFINE hindcast database. This not only ensures design criteria that are statistically and spatially consistent but is also faster by avoiding the need for repetitive site-specific analysis. We have to overcome several challenges before we can apply a large-scale extreme model on the SEAFINE database. These include identifying a spatially consistent set of storm peaks and validating the hindcast data with real measurements. We then estimate marginal return values for significant wave height for locations within a large spatial neighbourhood, accounting for spatial and storm directional variability of peaks over threshold. A quantile regression identifies the extreme value threshold, the rate of exceedance of which is described using a Poisson process. The size of threshold exceedances is described by a generalised Pareto model. The characteristics of the threshold, rate and size models are all non-stationary with respect to directional and spatial covariates, parameterised in terms of (multidimensional) penalised B-splines. Parameter estimation is computationally challenging, but a combination of efficient generalised linear array algorithms executed within a parallel computing environment enable maximum likelihood estimation of all models. Bootstrap resampling is used to estimate uncertainties of model parameters and return values. We thus estimate consistent marginal return values for significant wave height and their uncertainties, at all locations in the spatial neighbourhood. In addition, we quantify directional variability of return values across different return periods. We rigorously validate the proposed spatio-directional model with that of a direction-only model by deriving model diagnostics for the same site and demonstrating equivalent goodness of fits. To our knowledge this is the first of a kind of application of large-scale estimation for the South China Sea. With this approach, design criteria for large spatial domains, non-stationary with respect to the appropriate environmental covariates, can be estimated efficiently, consistently and with quantified uncertainty. {\textcopyright} 2016, Offshore Technology Conference",
keywords = "Maximum likelihood estimation, Storms, Water waves, Bootstrap resampling, Model diagnostics, Offshore facilities, Parallel-computing environment, Peaks over threshold, Quantile regression, Real measurements, Significant wave height, Uncertainty analysis",
author = "L. Raghupathi and D. Randell and P. Jonathan and K.C. Ewans",
year = "2016",
doi = "10.4043/26668-MS",
language = "English",
pages = "844--863",
note = "Offshore Technology Conference-Asia ; Conference date: 25-03-2014 Through 28-03-2014",

}

RIS

TY - CONF

T1 - Consistent design criteria for south China sea with a large-scale extreme value model

AU - Raghupathi, L.

AU - Randell, D.

AU - Jonathan, P.

AU - Ewans, K.C.

PY - 2016

Y1 - 2016

N2 - Existing metocean design criteria for offshore facilities in the South China Sea have been estimated using different data and procedures, some of which are at least partly ad hoc. As a result, it is probable that existing criteria are inconsistent, in the sense that assets designed to the same design codes have different realised levels of integrity. To address this concern in this paper, we apply a large-scale extreme value model adapted to parallel computing environment, applied to the recent high-resolution SEAFINE hindcast database. This not only ensures design criteria that are statistically and spatially consistent but is also faster by avoiding the need for repetitive site-specific analysis. We have to overcome several challenges before we can apply a large-scale extreme model on the SEAFINE database. These include identifying a spatially consistent set of storm peaks and validating the hindcast data with real measurements. We then estimate marginal return values for significant wave height for locations within a large spatial neighbourhood, accounting for spatial and storm directional variability of peaks over threshold. A quantile regression identifies the extreme value threshold, the rate of exceedance of which is described using a Poisson process. The size of threshold exceedances is described by a generalised Pareto model. The characteristics of the threshold, rate and size models are all non-stationary with respect to directional and spatial covariates, parameterised in terms of (multidimensional) penalised B-splines. Parameter estimation is computationally challenging, but a combination of efficient generalised linear array algorithms executed within a parallel computing environment enable maximum likelihood estimation of all models. Bootstrap resampling is used to estimate uncertainties of model parameters and return values. We thus estimate consistent marginal return values for significant wave height and their uncertainties, at all locations in the spatial neighbourhood. In addition, we quantify directional variability of return values across different return periods. We rigorously validate the proposed spatio-directional model with that of a direction-only model by deriving model diagnostics for the same site and demonstrating equivalent goodness of fits. To our knowledge this is the first of a kind of application of large-scale estimation for the South China Sea. With this approach, design criteria for large spatial domains, non-stationary with respect to the appropriate environmental covariates, can be estimated efficiently, consistently and with quantified uncertainty. © 2016, Offshore Technology Conference

AB - Existing metocean design criteria for offshore facilities in the South China Sea have been estimated using different data and procedures, some of which are at least partly ad hoc. As a result, it is probable that existing criteria are inconsistent, in the sense that assets designed to the same design codes have different realised levels of integrity. To address this concern in this paper, we apply a large-scale extreme value model adapted to parallel computing environment, applied to the recent high-resolution SEAFINE hindcast database. This not only ensures design criteria that are statistically and spatially consistent but is also faster by avoiding the need for repetitive site-specific analysis. We have to overcome several challenges before we can apply a large-scale extreme model on the SEAFINE database. These include identifying a spatially consistent set of storm peaks and validating the hindcast data with real measurements. We then estimate marginal return values for significant wave height for locations within a large spatial neighbourhood, accounting for spatial and storm directional variability of peaks over threshold. A quantile regression identifies the extreme value threshold, the rate of exceedance of which is described using a Poisson process. The size of threshold exceedances is described by a generalised Pareto model. The characteristics of the threshold, rate and size models are all non-stationary with respect to directional and spatial covariates, parameterised in terms of (multidimensional) penalised B-splines. Parameter estimation is computationally challenging, but a combination of efficient generalised linear array algorithms executed within a parallel computing environment enable maximum likelihood estimation of all models. Bootstrap resampling is used to estimate uncertainties of model parameters and return values. We thus estimate consistent marginal return values for significant wave height and their uncertainties, at all locations in the spatial neighbourhood. In addition, we quantify directional variability of return values across different return periods. We rigorously validate the proposed spatio-directional model with that of a direction-only model by deriving model diagnostics for the same site and demonstrating equivalent goodness of fits. To our knowledge this is the first of a kind of application of large-scale estimation for the South China Sea. With this approach, design criteria for large spatial domains, non-stationary with respect to the appropriate environmental covariates, can be estimated efficiently, consistently and with quantified uncertainty. © 2016, Offshore Technology Conference

KW - Maximum likelihood estimation

KW - Storms

KW - Water waves

KW - Bootstrap resampling

KW - Model diagnostics

KW - Offshore facilities

KW - Parallel-computing environment

KW - Peaks over threshold

KW - Quantile regression

KW - Real measurements

KW - Significant wave height

KW - Uncertainty analysis

U2 - 10.4043/26668-MS

DO - 10.4043/26668-MS

M3 - Conference paper

SP - 844

EP - 863

T2 - Offshore Technology Conference-Asia

Y2 - 25 March 2014 through 28 March 2014

ER -