Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach

Autores
Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Several socio-economic sectors are sensitive to the occurrence of extreme climate events. The ability to predict these extremes will allow precautionary measures to reduce their impacts. This work aims to disseminate a seasonal statistical forecast of daily temperature extremes in Argentina to the international scientific community. At the local level, this forecast is shared at monthly meetings organized by the Argentine National Meteorological Service and attended by different users. For the temperature extremes modeling, several predictors and statistical techniques were applied. We estimated the probability of each tercile category (above-normal, near-normal, and below-normal) by quantifying the percentage of models that predict each of them. The forecasts were verified by calculating different metrics. In general, we observed that the forecast system has less skill to discriminate the near-normal category in all seasons, and the other categories present a skill highly variable according to the season, region, and extreme index. The verification process revealed that predictability increases for all extreme indices with a previous La Niña phase. This product represents an advance towards an operational seasonal forecast of extreme temperatures in Argentina because it offers predictions based on a detailed study of predictors in the region, the incorporation of multiple statistical methodologies, and the predicted variables are not the most typical ones offered by forecasting centers. Finally, it is highlighted that the accuracy rate obtained with this product exceeds a forecast based on climatology, i.e., despite the uncertainties, our forecasts provide additional information to users for decision making.
Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Materia
ARGENTINA
EMPIRICAL MODELS
EXTREME TEMPERATURES
PREDICTORS
SEASONAL PREDICTION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/223631
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approachCollazo, Soledad MaribelBarrucand, Mariana GracielaRusticucci, Matilde MonicaARGENTINAEMPIRICAL MODELSEXTREME TEMPERATURESPREDICTORSSEASONAL PREDICTIONhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Several socio-economic sectors are sensitive to the occurrence of extreme climate events. The ability to predict these extremes will allow precautionary measures to reduce their impacts. This work aims to disseminate a seasonal statistical forecast of daily temperature extremes in Argentina to the international scientific community. At the local level, this forecast is shared at monthly meetings organized by the Argentine National Meteorological Service and attended by different users. For the temperature extremes modeling, several predictors and statistical techniques were applied. We estimated the probability of each tercile category (above-normal, near-normal, and below-normal) by quantifying the percentage of models that predict each of them. The forecasts were verified by calculating different metrics. In general, we observed that the forecast system has less skill to discriminate the near-normal category in all seasons, and the other categories present a skill highly variable according to the season, region, and extreme index. The verification process revealed that predictability increases for all extreme indices with a previous La Niña phase. This product represents an advance towards an operational seasonal forecast of extreme temperatures in Argentina because it offers predictions based on a detailed study of predictors in the region, the incorporation of multiple statistical methodologies, and the predicted variables are not the most typical ones offered by forecasting centers. Finally, it is highlighted that the accuracy rate obtained with this product exceeds a forecast based on climatology, i.e., despite the uncertainties, our forecasts provide additional information to users for decision making.Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaFil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; ArgentinaElsevier2022-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/223631Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach; Elsevier; Climate Services; 26; 4-2022; 1-182405-8807CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2405880722000115info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cliser.2022.100293info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:10:03Zoai:ri.conicet.gov.ar:11336/223631instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:10:04.281CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
title Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
spellingShingle Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
Collazo, Soledad Maribel
ARGENTINA
EMPIRICAL MODELS
EXTREME TEMPERATURES
PREDICTORS
SEASONAL PREDICTION
title_short Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
title_full Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
title_fullStr Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
title_full_unstemmed Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
title_sort Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach
dc.creator.none.fl_str_mv Collazo, Soledad Maribel
Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author Collazo, Soledad Maribel
author_facet Collazo, Soledad Maribel
Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author_role author
author2 Barrucand, Mariana Graciela
Rusticucci, Matilde Monica
author2_role author
author
dc.subject.none.fl_str_mv ARGENTINA
EMPIRICAL MODELS
EXTREME TEMPERATURES
PREDICTORS
SEASONAL PREDICTION
topic ARGENTINA
EMPIRICAL MODELS
EXTREME TEMPERATURES
PREDICTORS
SEASONAL PREDICTION
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Several socio-economic sectors are sensitive to the occurrence of extreme climate events. The ability to predict these extremes will allow precautionary measures to reduce their impacts. This work aims to disseminate a seasonal statistical forecast of daily temperature extremes in Argentina to the international scientific community. At the local level, this forecast is shared at monthly meetings organized by the Argentine National Meteorological Service and attended by different users. For the temperature extremes modeling, several predictors and statistical techniques were applied. We estimated the probability of each tercile category (above-normal, near-normal, and below-normal) by quantifying the percentage of models that predict each of them. The forecasts were verified by calculating different metrics. In general, we observed that the forecast system has less skill to discriminate the near-normal category in all seasons, and the other categories present a skill highly variable according to the season, region, and extreme index. The verification process revealed that predictability increases for all extreme indices with a previous La Niña phase. This product represents an advance towards an operational seasonal forecast of extreme temperatures in Argentina because it offers predictions based on a detailed study of predictors in the region, the incorporation of multiple statistical methodologies, and the predicted variables are not the most typical ones offered by forecasting centers. Finally, it is highlighted that the accuracy rate obtained with this product exceeds a forecast based on climatology, i.e., despite the uncertainties, our forecasts provide additional information to users for decision making.
Fil: Collazo, Soledad Maribel. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Barrucand, Mariana Graciela. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
Fil: Rusticucci, Matilde Monica. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Departamento de Ciencias de la Atmósfera y los Océanos; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria; Argentina
description Several socio-economic sectors are sensitive to the occurrence of extreme climate events. The ability to predict these extremes will allow precautionary measures to reduce their impacts. This work aims to disseminate a seasonal statistical forecast of daily temperature extremes in Argentina to the international scientific community. At the local level, this forecast is shared at monthly meetings organized by the Argentine National Meteorological Service and attended by different users. For the temperature extremes modeling, several predictors and statistical techniques were applied. We estimated the probability of each tercile category (above-normal, near-normal, and below-normal) by quantifying the percentage of models that predict each of them. The forecasts were verified by calculating different metrics. In general, we observed that the forecast system has less skill to discriminate the near-normal category in all seasons, and the other categories present a skill highly variable according to the season, region, and extreme index. The verification process revealed that predictability increases for all extreme indices with a previous La Niña phase. This product represents an advance towards an operational seasonal forecast of extreme temperatures in Argentina because it offers predictions based on a detailed study of predictors in the region, the incorporation of multiple statistical methodologies, and the predicted variables are not the most typical ones offered by forecasting centers. Finally, it is highlighted that the accuracy rate obtained with this product exceeds a forecast based on climatology, i.e., despite the uncertainties, our forecasts provide additional information to users for decision making.
publishDate 2022
dc.date.none.fl_str_mv 2022-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/223631
Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach; Elsevier; Climate Services; 26; 4-2022; 1-18
2405-8807
CONICET Digital
CONICET
url http://hdl.handle.net/11336/223631
identifier_str_mv Collazo, Soledad Maribel; Barrucand, Mariana Graciela; Rusticucci, Matilde Monica; Seasonal forecast of the percentage of days with extreme temperatures in central-northern Argentina: An operational statistical approach; Elsevier; Climate Services; 26; 4-2022; 1-18
2405-8807
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S2405880722000115
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.cliser.2022.100293
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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