Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina

Autores
Hurtado, Santiago Ignacio; Zaninelli, Pablo Gabriel; Agosta, Eduardo A.; Ricetti, Lorenzo
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Precipitation plays a crucial role from a social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous and reliable precipitation records to develop serious climatological researches. However, precipitation records in this region are frequently inhomogeneous and scarce, which makes it necessary to deal with data filling methods. Choosing the best method to complete precipitation data series relies on rain gauge network density and on the complexity of orography, among other factors. Most comparative-method studies in the literature are focused on dense station networks while, contrastingly, the STAr's station network density is remarkably poor (between 10 and 1000 times lower). The research aims at assessing the performance of several interpolation methods in STAr. In this sense, the performance of a large number of interpolation methods was evaluated for dry and wet seasons, interpolating raw monthly data and their anomalies applied to different time-series subsets. In general, most methods performances improve when applied to anomalies in the seasonal time-series subset. Multiple Linear Regression (MLR) stands out as the method with the best performance for infilling precipitation records for most of the regions regardless of orography or season. Despite the bibliography invokes that kriging interpolation methods are the best ones, in this work the performance of kriging methods was similar to the one of the Inverse Distance Weighted method (IDW) and the Angular Distance Weighted method (ADW, the method used to generate CRU precipitation dataset).
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Zaninelli, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina
Fil: Agosta, Eduardo A.. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Materia
INTERPOLATION METHODS
MISSING DATA
MONTHLY PRECIPITATION
SCARCE DATA
TIME SERIES
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:11336/163465
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/163465
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network_name_str CONICET Digital (CONICET)
spelling Infilling methods for monthly precipitation records with poor station network density in Subtropical ArgentinaHurtado, Santiago IgnacioZaninelli, Pablo GabrielAgosta, Eduardo A.Ricetti, LorenzoINTERPOLATION METHODSMISSING DATAMONTHLY PRECIPITATIONSCARCE DATATIME SERIEShttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1Precipitation plays a crucial role from a social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous and reliable precipitation records to develop serious climatological researches. However, precipitation records in this region are frequently inhomogeneous and scarce, which makes it necessary to deal with data filling methods. Choosing the best method to complete precipitation data series relies on rain gauge network density and on the complexity of orography, among other factors. Most comparative-method studies in the literature are focused on dense station networks while, contrastingly, the STAr's station network density is remarkably poor (between 10 and 1000 times lower). The research aims at assessing the performance of several interpolation methods in STAr. In this sense, the performance of a large number of interpolation methods was evaluated for dry and wet seasons, interpolating raw monthly data and their anomalies applied to different time-series subsets. In general, most methods performances improve when applied to anomalies in the seasonal time-series subset. Multiple Linear Regression (MLR) stands out as the method with the best performance for infilling precipitation records for most of the regions regardless of orography or season. Despite the bibliography invokes that kriging interpolation methods are the best ones, in this work the performance of kriging methods was similar to the one of the Inverse Distance Weighted method (IDW) and the Angular Distance Weighted method (ADW, the method used to generate CRU precipitation dataset).Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Zaninelli, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; ArgentinaFil: Agosta, Eduardo A.. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaFil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; ArgentinaElsevier2021-06info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/163465Hurtado, Santiago Ignacio; Zaninelli, Pablo Gabriel; Agosta, Eduardo A.; Ricetti, Lorenzo; Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina; Elsevier; Atmospheric Research; 254; 6-2021; 1-340169-8095CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://linkinghub.elsevier.com/retrieve/pii/S016980952100034Xinfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.atmosres.2021.105482info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T09:59:02Zoai:ri.conicet.gov.ar:11336/163465instacron: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 09:59:02.933CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
title Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
spellingShingle Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
Hurtado, Santiago Ignacio
INTERPOLATION METHODS
MISSING DATA
MONTHLY PRECIPITATION
SCARCE DATA
TIME SERIES
title_short Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
title_full Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
title_fullStr Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
title_full_unstemmed Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
title_sort Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina
dc.creator.none.fl_str_mv Hurtado, Santiago Ignacio
Zaninelli, Pablo Gabriel
Agosta, Eduardo A.
Ricetti, Lorenzo
author Hurtado, Santiago Ignacio
author_facet Hurtado, Santiago Ignacio
Zaninelli, Pablo Gabriel
Agosta, Eduardo A.
Ricetti, Lorenzo
author_role author
author2 Zaninelli, Pablo Gabriel
Agosta, Eduardo A.
Ricetti, Lorenzo
author2_role author
author
author
dc.subject.none.fl_str_mv INTERPOLATION METHODS
MISSING DATA
MONTHLY PRECIPITATION
SCARCE DATA
TIME SERIES
topic INTERPOLATION METHODS
MISSING DATA
MONTHLY PRECIPITATION
SCARCE DATA
TIME SERIES
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Precipitation plays a crucial role from a social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous and reliable precipitation records to develop serious climatological researches. However, precipitation records in this region are frequently inhomogeneous and scarce, which makes it necessary to deal with data filling methods. Choosing the best method to complete precipitation data series relies on rain gauge network density and on the complexity of orography, among other factors. Most comparative-method studies in the literature are focused on dense station networks while, contrastingly, the STAr's station network density is remarkably poor (between 10 and 1000 times lower). The research aims at assessing the performance of several interpolation methods in STAr. In this sense, the performance of a large number of interpolation methods was evaluated for dry and wet seasons, interpolating raw monthly data and their anomalies applied to different time-series subsets. In general, most methods performances improve when applied to anomalies in the seasonal time-series subset. Multiple Linear Regression (MLR) stands out as the method with the best performance for infilling precipitation records for most of the regions regardless of orography or season. Despite the bibliography invokes that kriging interpolation methods are the best ones, in this work the performance of kriging methods was similar to the one of the Inverse Distance Weighted method (IDW) and the Angular Distance Weighted method (ADW, the method used to generate CRU precipitation dataset).
Fil: Hurtado, Santiago Ignacio. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; Argentina. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Zaninelli, Pablo Gabriel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina. Instituto Franco-Argentino sobre Estudios del Clima y sus Impactos; Argentina
Fil: Agosta, Eduardo A.. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
Fil: Ricetti, Lorenzo. Universidad Nacional de La Plata. Facultad de Ciencias Astronómicas y Geofísicas; Argentina
description Precipitation plays a crucial role from a social and economic perspective in Subtropical Argentina (STAr). Therefore, it renders the need for continuous and reliable precipitation records to develop serious climatological researches. However, precipitation records in this region are frequently inhomogeneous and scarce, which makes it necessary to deal with data filling methods. Choosing the best method to complete precipitation data series relies on rain gauge network density and on the complexity of orography, among other factors. Most comparative-method studies in the literature are focused on dense station networks while, contrastingly, the STAr's station network density is remarkably poor (between 10 and 1000 times lower). The research aims at assessing the performance of several interpolation methods in STAr. In this sense, the performance of a large number of interpolation methods was evaluated for dry and wet seasons, interpolating raw monthly data and their anomalies applied to different time-series subsets. In general, most methods performances improve when applied to anomalies in the seasonal time-series subset. Multiple Linear Regression (MLR) stands out as the method with the best performance for infilling precipitation records for most of the regions regardless of orography or season. Despite the bibliography invokes that kriging interpolation methods are the best ones, in this work the performance of kriging methods was similar to the one of the Inverse Distance Weighted method (IDW) and the Angular Distance Weighted method (ADW, the method used to generate CRU precipitation dataset).
publishDate 2021
dc.date.none.fl_str_mv 2021-06
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/11336/163465
Hurtado, Santiago Ignacio; Zaninelli, Pablo Gabriel; Agosta, Eduardo A.; Ricetti, Lorenzo; Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina; Elsevier; Atmospheric Research; 254; 6-2021; 1-34
0169-8095
CONICET Digital
CONICET
url http://hdl.handle.net/11336/163465
identifier_str_mv Hurtado, Santiago Ignacio; Zaninelli, Pablo Gabriel; Agosta, Eduardo A.; Ricetti, Lorenzo; Infilling methods for monthly precipitation records with poor station network density in Subtropical Argentina; Elsevier; Atmospheric Research; 254; 6-2021; 1-34
0169-8095
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/S016980952100034X
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.atmosres.2021.105482
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
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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