Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes
A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple...
Published in: | Environmental Research Letters |
---|---|
Main Authors: | , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
IOP Publishing
2023
|
Subjects: | |
Online Access: | https://doi.org/10.1088/1748-9326/acb6aa https://doaj.org/article/1d395e67767c40caa275502873d7ef37 |
id |
ftdoajarticles:oai:doaj.org/article:1d395e67767c40caa275502873d7ef37 |
---|---|
record_format |
openpolar |
spelling |
ftdoajarticles:oai:doaj.org/article:1d395e67767c40caa275502873d7ef37 2023-09-05T13:21:45+02:00 Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes Paulina Tedesco Alex Lenkoski Hannah C Bloomfield Jana Sillmann 2023-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/acb6aa https://doaj.org/article/1d395e67767c40caa275502873d7ef37 EN eng IOP Publishing https://doi.org/10.1088/1748-9326/acb6aa https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/acb6aa 1748-9326 https://doaj.org/article/1d395e67767c40caa275502873d7ef37 Environmental Research Letters, Vol 18, Iss 3, p 034008 (2023) Gaussian copula extreme compound event logistic regression weather regimes renewable energy wind power Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2023 ftdoajarticles https://doi.org/10.1088/1748-9326/acb6aa 2023-08-13T00:36:58Z A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid $(0.2^\circ)$ .Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Environmental Research Letters 18 3 034008 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
Gaussian copula extreme compound event logistic regression weather regimes renewable energy wind power Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
Gaussian copula extreme compound event logistic regression weather regimes renewable energy wind power Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Paulina Tedesco Alex Lenkoski Hannah C Bloomfield Jana Sillmann Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
topic_facet |
Gaussian copula extreme compound event logistic regression weather regimes renewable energy wind power Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
description |
A transition to renewable energy is needed to mitigate climate change. In Europe, this transition has been led by wind energy, which is one of the fastest growing energy sources. However, energy demand and production are sensitive to meteorological conditions and atmospheric variability at multiple time scales. To accomplish the required balance between these two variables, critical conditions of high demand and low wind energy supply must be considered in the design of energy systems. We describe a methodology for modeling joint distributions of meteorological variables without making any assumptions about their marginal distributions. In this context, Gaussian copulas are used to model the correlated nature of cold and weak-wind events. The marginal distributions are modeled with logistic regressions defining two sets of binary variables as predictors: four large-scale weather regimes (WRs) and the months of the extended winter season. By applying this framework to ERA5 data, we can compute the joint probabilities of co-occurrence of cold and weak-wind events on a high-resolution grid $(0.2^\circ)$ .Our results show that (a) WRs must be considered when modeling cold and weak-wind events, (b) it is essential to account for the correlations between these events when modeling their joint distribution, (c) we need to analyze each month separately, and (d) the highest estimated number of days with compound events are associated with the negative phase of the North Atlantic Oscillation (3 days on average over Finland, Ireland, and Lithuania in January, and France and Luxembourg in February) and the Scandinavian blocking pattern (3 days on average over Ireland in January and Denmark in February). This information could be relevant for application in sub-seasonal to seasonal forecasts of such events. |
format |
Article in Journal/Newspaper |
author |
Paulina Tedesco Alex Lenkoski Hannah C Bloomfield Jana Sillmann |
author_facet |
Paulina Tedesco Alex Lenkoski Hannah C Bloomfield Jana Sillmann |
author_sort |
Paulina Tedesco |
title |
Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
title_short |
Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
title_full |
Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
title_fullStr |
Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
title_full_unstemmed |
Gaussian copula modeling of extreme cold and weak-wind events over Europe conditioned on winter weather regimes |
title_sort |
gaussian copula modeling of extreme cold and weak-wind events over europe conditioned on winter weather regimes |
publisher |
IOP Publishing |
publishDate |
2023 |
url |
https://doi.org/10.1088/1748-9326/acb6aa https://doaj.org/article/1d395e67767c40caa275502873d7ef37 |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Environmental Research Letters, Vol 18, Iss 3, p 034008 (2023) |
op_relation |
https://doi.org/10.1088/1748-9326/acb6aa https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/acb6aa 1748-9326 https://doaj.org/article/1d395e67767c40caa275502873d7ef37 |
op_doi |
https://doi.org/10.1088/1748-9326/acb6aa |
container_title |
Environmental Research Letters |
container_volume |
18 |
container_issue |
3 |
container_start_page |
034008 |
_version_ |
1776202329625722880 |