A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery

Autores
Yuchechen, Adrian Enrique; Lakkis, Susan Gabriela; Caferri, Agustín; Canziani, Pablo Osvaldo; Muszkats, Juan Pablo
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.
Fil: Yuchechen, Adrian Enrique. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lakkis, Susan Gabriela. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Caferri, Agustín. No especifíca;
Fil: Canziani, Pablo Osvaldo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muszkats, Juan Pablo. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Departamento de Ciencias Básicas y Experimentales; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Materia
BRIGHTNESS TEMPERATURE
CLOUD COVER
CLOUD REGIMES
CLUSTERING
GOES IR IMAGERY
KMEANS
KMEANS++
SOUTH AMERICA
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/151331
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/151331
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imageryYuchechen, Adrian EnriqueLakkis, Susan GabrielaCaferri, AgustínCanziani, Pablo OsvaldoMuszkats, Juan PabloBRIGHTNESS TEMPERATURECLOUD COVERCLOUD REGIMESCLUSTERINGGOES IR IMAGERYKMEANSKMEANS++SOUTH AMERICAhttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.Fil: Yuchechen, Adrian Enrique. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Lakkis, Susan Gabriela. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Caferri, Agustín. No especifíca;Fil: Canziani, Pablo Osvaldo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Muszkats, Juan Pablo. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Departamento de Ciencias Básicas y Experimentales; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaMolecular Diversity Preservation International2020-09info: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/11336/151331Yuchechen, Adrian Enrique; Lakkis, Susan Gabriela; Caferri, Agustín; Canziani, Pablo Osvaldo; Muszkats, Juan Pablo; A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery; Molecular Diversity Preservation International; Remote Sensing; 12; 18; 9-2020; 1-302072-4292CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.mdpi.com/2072-4292/12/18/2991info:eu-repo/semantics/altIdentifier/doi/10.3390/rs12182991info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:21:28Zoai:ri.conicet.gov.ar:11336/151331instacron: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:34982025-10-22 11:21:28.827CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
title A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
spellingShingle A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
Yuchechen, Adrian Enrique
BRIGHTNESS TEMPERATURE
CLOUD COVER
CLOUD REGIMES
CLUSTERING
GOES IR IMAGERY
KMEANS
KMEANS++
SOUTH AMERICA
title_short A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
title_full A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
title_fullStr A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
title_full_unstemmed A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
title_sort A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery
dc.creator.none.fl_str_mv Yuchechen, Adrian Enrique
Lakkis, Susan Gabriela
Caferri, Agustín
Canziani, Pablo Osvaldo
Muszkats, Juan Pablo
author Yuchechen, Adrian Enrique
author_facet Yuchechen, Adrian Enrique
Lakkis, Susan Gabriela
Caferri, Agustín
Canziani, Pablo Osvaldo
Muszkats, Juan Pablo
author_role author
author2 Lakkis, Susan Gabriela
Caferri, Agustín
Canziani, Pablo Osvaldo
Muszkats, Juan Pablo
author2_role author
author
author
author
dc.subject.none.fl_str_mv BRIGHTNESS TEMPERATURE
CLOUD COVER
CLOUD REGIMES
CLUSTERING
GOES IR IMAGERY
KMEANS
KMEANS++
SOUTH AMERICA
topic BRIGHTNESS TEMPERATURE
CLOUD COVER
CLOUD REGIMES
CLUSTERING
GOES IR IMAGERY
KMEANS
KMEANS++
SOUTH AMERICA
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.
Fil: Yuchechen, Adrian Enrique. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Lakkis, Susan Gabriela. Pontificia Universidad Católica Argentina "Santa María de los Buenos Aires"; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Caferri, Agustín. No especifíca;
Fil: Canziani, Pablo Osvaldo. Universidad Tecnológica Nacional. Facultad Regional Buenos Aires. Unidad de Investigación y Desarrollo de las Ingenierías; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Muszkats, Juan Pablo. Universidad Nacional del Noroeste de la Provincia de Buenos Aires. Departamento de Ciencias Básicas y Experimentales; Argentina. Universidad de Buenos Aires; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
description An unsupervised k-means/k-means++ clustering algorithm was implemented on daily images of standardized anomalies of brightness temperature (Tb) derived from the Geostationary Operational Environmental Satellite (GOES)-13 infrared data for the period 1 December 2010 to 30 November 2016. The goal was to decompose each individual Tb image into four clusters that captures the characteristics of different cloud regimes. The extracted clusters were ordered by their mean value in an ascending fashion so that the lower the cluster order, the higher the clouds they represent. A linear regression between temperature and height with temperature used as the predictor was conducted to estimate cloud top heights (CTHs) from the Tb values. The analysis of the results was performed in two different ways: sample dates and seasonal features. Cluster 1 is the less dominant one, representing clouds with the highest tops and variabilities. Cluster 4 is the most dominant one and represents a cloud regime that spans the lowest 2 km of the troposphere. Clusters 2 and 3 are entangled in the sense that both have their CTHs spanning the middle troposphere. Correlations between the monthly time series of the number of pixels in each cluster and of the entropy with several circulation indices are also introduced. Additionally, a fractal-related analysis was carried out on cluster 1 in order to resolve cirrus and cumulonimbus.
publishDate 2020
dc.date.none.fl_str_mv 2020-09
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/11336/151331
Yuchechen, Adrian Enrique; Lakkis, Susan Gabriela; Caferri, Agustín; Canziani, Pablo Osvaldo; Muszkats, Juan Pablo; A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery; Molecular Diversity Preservation International; Remote Sensing; 12; 18; 9-2020; 1-30
2072-4292
CONICET Digital
CONICET
url http://hdl.handle.net/11336/151331
identifier_str_mv Yuchechen, Adrian Enrique; Lakkis, Susan Gabriela; Caferri, Agustín; Canziani, Pablo Osvaldo; Muszkats, Juan Pablo; A cluster approach to cloud cover classification over South America and adjacent oceans using a k-means/k-means++ unsupervised algorithm on GOES IR imagery; Molecular Diversity Preservation International; Remote Sensing; 12; 18; 9-2020; 1-30
2072-4292
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://www.mdpi.com/2072-4292/12/18/2991
info:eu-repo/semantics/altIdentifier/doi/10.3390/rs12182991
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Molecular Diversity Preservation International
publisher.none.fl_str_mv Molecular Diversity Preservation International
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
_version_ 1846781718001876992
score 12.982451

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