Storm identification for high-energy wave climates as a tool to improve long-term analysis

Detalhes bibliográficos
Autor(a) principal: Kümmerer, Vincent
Data de Publicação: 2023
Outros Autores: Ferreira, Óscar, Fanti, Valeria, Loureiro, C.
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.1/20269
Resumo: Coastal storms can cause erosion and flooding of coastal areas, often accompanied by significant social-economic disruption. As such, storm characterisation is crucial for an improved understanding of storm impacts and thus for coastal management. However, storm definitions are commonly different between authors, and storm thresholds are often selected arbitrarily, with the statistical and meteorological independence between storm events frequently being neglected. In this work, a storm identification algorithm based on statistically defined criteria was developed to identify independent storms in time series of significant wave height for high wave energy environments. This approach proposes a minimum duration between storms determined using the extremal index. The performance of the storm identification algorithm was tested against the commonly used peak-over-threshold. Both approaches were applied to 40 and 70-year-long calibrated wave reanalyses datasets for Western Scotland, where the intense and rapid succession of extratropical storms during the winter makes the identification of independent storm events notably challenging. The storm identification algorithm provides results that are consistent with regional meteorological processes and timescales, allowing to separate independent storms during periods of rapid storm succession, enabling an objective and robust storm characterisation. Identifying storms and their characteristics using the proposed algorithm allowed to determine a statistically significant increasing long-term trend in storm duration, which contributes to the increase in storm wave power in the west of Scotland. The coastal storm identification algorithm is found to be particularly suitable for high-energy, storm-dominated coastal environments, such as those located along the main global extratropical storm tracks.
id RCAP_d2b3a59e5abc246bdb3c9c14f43a80db
oai_identifier_str oai:sapientia.ualg.pt:10400.1/20269
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Storm identification for high-energy wave climates as a tool to improve long-term analysisCoastal stormStorm independenceWave reanalysisWave powerNortheast AtlanticWestern ScotlandCoastal storms can cause erosion and flooding of coastal areas, often accompanied by significant social-economic disruption. As such, storm characterisation is crucial for an improved understanding of storm impacts and thus for coastal management. However, storm definitions are commonly different between authors, and storm thresholds are often selected arbitrarily, with the statistical and meteorological independence between storm events frequently being neglected. In this work, a storm identification algorithm based on statistically defined criteria was developed to identify independent storms in time series of significant wave height for high wave energy environments. This approach proposes a minimum duration between storms determined using the extremal index. The performance of the storm identification algorithm was tested against the commonly used peak-over-threshold. Both approaches were applied to 40 and 70-year-long calibrated wave reanalyses datasets for Western Scotland, where the intense and rapid succession of extratropical storms during the winter makes the identification of independent storm events notably challenging. The storm identification algorithm provides results that are consistent with regional meteorological processes and timescales, allowing to separate independent storms during periods of rapid storm succession, enabling an objective and robust storm characterisation. Identifying storms and their characteristics using the proposed algorithm allowed to determine a statistically significant increasing long-term trend in storm duration, which contributes to the increase in storm wave power in the west of Scotland. The coastal storm identification algorithm is found to be particularly suitable for high-energy, storm-dominated coastal environments, such as those located along the main global extratropical storm tracks.Fundação para a Ciências e a Tecnologia (FCT) LA/P/00069/2020SpringerSapientiaKümmerer, VincentFerreira, ÓscarFanti, ValeriaLoureiro, C.2024-01-04T12:03:35Z2023-122023-12-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/20269eng10.1007/s00382-023-07017-w1432-0894info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-01-10T02:00:59Zoai:sapientia.ualg.pt:10400.1/20269Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:31:11.484413Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Storm identification for high-energy wave climates as a tool to improve long-term analysis
title Storm identification for high-energy wave climates as a tool to improve long-term analysis
spellingShingle Storm identification for high-energy wave climates as a tool to improve long-term analysis
Kümmerer, Vincent
Coastal storm
Storm independence
Wave reanalysis
Wave power
Northeast Atlantic
Western Scotland
title_short Storm identification for high-energy wave climates as a tool to improve long-term analysis
title_full Storm identification for high-energy wave climates as a tool to improve long-term analysis
title_fullStr Storm identification for high-energy wave climates as a tool to improve long-term analysis
title_full_unstemmed Storm identification for high-energy wave climates as a tool to improve long-term analysis
title_sort Storm identification for high-energy wave climates as a tool to improve long-term analysis
author Kümmerer, Vincent
author_facet Kümmerer, Vincent
Ferreira, Óscar
Fanti, Valeria
Loureiro, C.
author_role author
author2 Ferreira, Óscar
Fanti, Valeria
Loureiro, C.
author2_role author
author
author
dc.contributor.none.fl_str_mv Sapientia
dc.contributor.author.fl_str_mv Kümmerer, Vincent
Ferreira, Óscar
Fanti, Valeria
Loureiro, C.
dc.subject.por.fl_str_mv Coastal storm
Storm independence
Wave reanalysis
Wave power
Northeast Atlantic
Western Scotland
topic Coastal storm
Storm independence
Wave reanalysis
Wave power
Northeast Atlantic
Western Scotland
description Coastal storms can cause erosion and flooding of coastal areas, often accompanied by significant social-economic disruption. As such, storm characterisation is crucial for an improved understanding of storm impacts and thus for coastal management. However, storm definitions are commonly different between authors, and storm thresholds are often selected arbitrarily, with the statistical and meteorological independence between storm events frequently being neglected. In this work, a storm identification algorithm based on statistically defined criteria was developed to identify independent storms in time series of significant wave height for high wave energy environments. This approach proposes a minimum duration between storms determined using the extremal index. The performance of the storm identification algorithm was tested against the commonly used peak-over-threshold. Both approaches were applied to 40 and 70-year-long calibrated wave reanalyses datasets for Western Scotland, where the intense and rapid succession of extratropical storms during the winter makes the identification of independent storm events notably challenging. The storm identification algorithm provides results that are consistent with regional meteorological processes and timescales, allowing to separate independent storms during periods of rapid storm succession, enabling an objective and robust storm characterisation. Identifying storms and their characteristics using the proposed algorithm allowed to determine a statistically significant increasing long-term trend in storm duration, which contributes to the increase in storm wave power in the west of Scotland. The coastal storm identification algorithm is found to be particularly suitable for high-energy, storm-dominated coastal environments, such as those located along the main global extratropical storm tracks.
publishDate 2023
dc.date.none.fl_str_mv 2023-12
2023-12-01T00:00:00Z
2024-01-04T12:03:35Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.1/20269
url http://hdl.handle.net/10400.1/20269
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1007/s00382-023-07017-w
1432-0894
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799136793812008960