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    Rights statement: © 2017 Project Euclid

    Accepted author manuscript, 2.12 MB, PDF document

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Statistical downscaling for future extreme wave heights in the North Sea

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Published

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Statistical downscaling for future extreme wave heights in the North Sea. / Towe, Ross Paul; Eastoe, Emma Frances; Tawn, Jonathan Angus et al.
In: Annals of Applied Statistics, Vol. 11, No. 4, 01.09.2017, p. 2375–2403.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Towe, RP, Eastoe, EF, Tawn, JA & Jonathan, P 2017, 'Statistical downscaling for future extreme wave heights in the North Sea', Annals of Applied Statistics, vol. 11, no. 4, pp. 2375–2403. https://doi.org/10.1214/17-AOAS1084

APA

Towe, R. P., Eastoe, E. F., Tawn, J. A., & Jonathan, P. (2017). Statistical downscaling for future extreme wave heights in the North Sea. Annals of Applied Statistics, 11(4), 2375–2403. https://doi.org/10.1214/17-AOAS1084

Vancouver

Towe RP, Eastoe EF, Tawn JA, Jonathan P. Statistical downscaling for future extreme wave heights in the North Sea. Annals of Applied Statistics. 2017 Sept 1;11(4):2375–2403. Epub 2017 Aug 1. doi: 10.1214/17-AOAS1084

Author

Towe, Ross Paul ; Eastoe, Emma Frances ; Tawn, Jonathan Angus et al. / Statistical downscaling for future extreme wave heights in the North Sea. In: Annals of Applied Statistics. 2017 ; Vol. 11, No. 4. pp. 2375–2403.

Bibtex

@article{8b7597dd89414ac1ac0cce08a6c83087,
title = "Statistical downscaling for future extreme wave heights in the North Sea",
abstract = "For safe offshore operations accurate knowledge of the extreme oceanographic conditions is required. We develop a multi-step statistical downscaling algorithm using data from low resolution global climate model (GCM) and local-scale hindcast data to make predictions of the extreme wave climate in the next 50 year period at locations in the North Sea. The GCM is unable to produce wave data accurately so instead we use its 3-hourly wind speed and direction data. By exploiting the relationships between wind characteristics and wave heights, a downscaling approach is developed to relate the large and local-scale data sets and hence future changes in wind characteristics can be translated into changes in extreme wave distributions. We assess the performance of the methods using within sample testing and apply the method to derive future design levels over the northern North Sea.",
author = "Towe, {Ross Paul} and Eastoe, {Emma Frances} and Tawn, {Jonathan Angus} and Philip Jonathan",
note = "{\textcopyright} 2017 Project Euclid",
year = "2017",
month = sep,
day = "1",
doi = "10.1214/17-AOAS1084",
language = "English",
volume = "11",
pages = "2375–2403",
journal = "Annals of Applied Statistics",
issn = "1932-6157",
publisher = "Institute of Mathematical Statistics",
number = "4",

}

RIS

TY - JOUR

T1 - Statistical downscaling for future extreme wave heights in the North Sea

AU - Towe, Ross Paul

AU - Eastoe, Emma Frances

AU - Tawn, Jonathan Angus

AU - Jonathan, Philip

N1 - © 2017 Project Euclid

PY - 2017/9/1

Y1 - 2017/9/1

N2 - For safe offshore operations accurate knowledge of the extreme oceanographic conditions is required. We develop a multi-step statistical downscaling algorithm using data from low resolution global climate model (GCM) and local-scale hindcast data to make predictions of the extreme wave climate in the next 50 year period at locations in the North Sea. The GCM is unable to produce wave data accurately so instead we use its 3-hourly wind speed and direction data. By exploiting the relationships between wind characteristics and wave heights, a downscaling approach is developed to relate the large and local-scale data sets and hence future changes in wind characteristics can be translated into changes in extreme wave distributions. We assess the performance of the methods using within sample testing and apply the method to derive future design levels over the northern North Sea.

AB - For safe offshore operations accurate knowledge of the extreme oceanographic conditions is required. We develop a multi-step statistical downscaling algorithm using data from low resolution global climate model (GCM) and local-scale hindcast data to make predictions of the extreme wave climate in the next 50 year period at locations in the North Sea. The GCM is unable to produce wave data accurately so instead we use its 3-hourly wind speed and direction data. By exploiting the relationships between wind characteristics and wave heights, a downscaling approach is developed to relate the large and local-scale data sets and hence future changes in wind characteristics can be translated into changes in extreme wave distributions. We assess the performance of the methods using within sample testing and apply the method to derive future design levels over the northern North Sea.

U2 - 10.1214/17-AOAS1084

DO - 10.1214/17-AOAS1084

M3 - Journal article

VL - 11

SP - 2375

EP - 2403

JO - Annals of Applied Statistics

JF - Annals of Applied Statistics

SN - 1932-6157

IS - 4

ER -