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    Rights statement: This is the peer reviewed version of the following article: Winter, H. C. and Tawn, J. A. (2016), Modelling heatwaves in central France: a case-study in extremal dependence. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65: 345–365. doi: 10.1111/rssc.12121 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssc.12121/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

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Modelling heatwaves in central France: a case-study in extremal dependence

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Modelling heatwaves in central France: a case-study in extremal dependence. / Winter, Hugo; Tawn, Jonathan Angus.
In: Journal of the Royal Statistical Society: Series C (Applied Statistics), Vol. 65, No. 3, 04.2016, p. 345-365.

Research output: Contribution to Journal/MagazineJournal articlepeer-review

Harvard

Winter, H & Tawn, JA 2016, 'Modelling heatwaves in central France: a case-study in extremal dependence', Journal of the Royal Statistical Society: Series C (Applied Statistics), vol. 65, no. 3, pp. 345-365. https://doi.org/10.1111/rssc.12121

APA

Winter, H., & Tawn, J. A. (2016). Modelling heatwaves in central France: a case-study in extremal dependence. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65(3), 345-365. https://doi.org/10.1111/rssc.12121

Vancouver

Winter H, Tawn JA. Modelling heatwaves in central France: a case-study in extremal dependence. Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016 Apr;65(3):345-365. Epub 2015 Sept 22. doi: 10.1111/rssc.12121

Author

Winter, Hugo ; Tawn, Jonathan Angus. / Modelling heatwaves in central France : a case-study in extremal dependence. In: Journal of the Royal Statistical Society: Series C (Applied Statistics). 2016 ; Vol. 65, No. 3. pp. 345-365.

Bibtex

@article{ae62c24494254aeaadfede25806194c5,
title = "Modelling heatwaves in central France: a case-study in extremal dependence",
abstract = "Heatwaves are phenomena that have large social and economic consequences. Understanding and estimating the frequency of such events are of great importance to climate scientists and decision makers. Heatwaves are a type of extreme event which are by definition rare and as such there are few data in the historical record to help planners. Extreme value theory is a general framework from which inference can be drawn from extreme events. When modelling heatwaves it is important to take into account the intensity and duration of events above a critical level as well as the interaction between both factors. Most previous methods assume that the duration distribution is independent of the critical level that is used to define a heatwave: a shortcoming that can lead to incorrect inferences. The paper characterizes a novel method for analysing the temporal dependence of heatwaves with reference to observed temperatures from Orleans in central France. This method enables estimation of the probabilities for heatwave events irrespectively of whether the duration distribution is independent of the critical level. The methods are demonstrated by estimating the probability of an event more severe than the 2003 European heatwave or an event that causes a specified increase in mortality.",
keywords = "Conditional extremes, Extremal dependence, Heatwaves, Markov chain, Time series extremes",
author = "Hugo Winter and Tawn, {Jonathan Angus}",
note = "This is the peer reviewed version of the following article: Winter, H. C. and Tawn, J. A. (2016), Modelling heatwaves in central France: a case-study in extremal dependence. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65: 345–365. doi: 10.1111/rssc.12121 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssc.12121/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.",
year = "2016",
month = apr,
doi = "10.1111/rssc.12121",
language = "English",
volume = "65",
pages = "345--365",
journal = "Journal of the Royal Statistical Society: Series C (Applied Statistics)",
issn = "0035-9254",
publisher = "Wiley-Blackwell",
number = "3",

}

RIS

TY - JOUR

T1 - Modelling heatwaves in central France

T2 - a case-study in extremal dependence

AU - Winter, Hugo

AU - Tawn, Jonathan Angus

N1 - This is the peer reviewed version of the following article: Winter, H. C. and Tawn, J. A. (2016), Modelling heatwaves in central France: a case-study in extremal dependence. Journal of the Royal Statistical Society: Series C (Applied Statistics), 65: 345–365. doi: 10.1111/rssc.12121 which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1111/rssc.12121/abstract This article may be used for non-commercial purposes in accordance With Wiley Terms and Conditions for self-archiving.

PY - 2016/4

Y1 - 2016/4

N2 - Heatwaves are phenomena that have large social and economic consequences. Understanding and estimating the frequency of such events are of great importance to climate scientists and decision makers. Heatwaves are a type of extreme event which are by definition rare and as such there are few data in the historical record to help planners. Extreme value theory is a general framework from which inference can be drawn from extreme events. When modelling heatwaves it is important to take into account the intensity and duration of events above a critical level as well as the interaction between both factors. Most previous methods assume that the duration distribution is independent of the critical level that is used to define a heatwave: a shortcoming that can lead to incorrect inferences. The paper characterizes a novel method for analysing the temporal dependence of heatwaves with reference to observed temperatures from Orleans in central France. This method enables estimation of the probabilities for heatwave events irrespectively of whether the duration distribution is independent of the critical level. The methods are demonstrated by estimating the probability of an event more severe than the 2003 European heatwave or an event that causes a specified increase in mortality.

AB - Heatwaves are phenomena that have large social and economic consequences. Understanding and estimating the frequency of such events are of great importance to climate scientists and decision makers. Heatwaves are a type of extreme event which are by definition rare and as such there are few data in the historical record to help planners. Extreme value theory is a general framework from which inference can be drawn from extreme events. When modelling heatwaves it is important to take into account the intensity and duration of events above a critical level as well as the interaction between both factors. Most previous methods assume that the duration distribution is independent of the critical level that is used to define a heatwave: a shortcoming that can lead to incorrect inferences. The paper characterizes a novel method for analysing the temporal dependence of heatwaves with reference to observed temperatures from Orleans in central France. This method enables estimation of the probabilities for heatwave events irrespectively of whether the duration distribution is independent of the critical level. The methods are demonstrated by estimating the probability of an event more severe than the 2003 European heatwave or an event that causes a specified increase in mortality.

KW - Conditional extremes

KW - Extremal dependence

KW - Heatwaves

KW - Markov chain

KW - Time series extremes

U2 - 10.1111/rssc.12121

DO - 10.1111/rssc.12121

M3 - Journal article

VL - 65

SP - 345

EP - 365

JO - Journal of the Royal Statistical Society: Series C (Applied Statistics)

JF - Journal of the Royal Statistical Society: Series C (Applied Statistics)

SN - 0035-9254

IS - 3

ER -