Missing data imputation of climate datasets: implications to modeling extreme drought events

Detalhes bibliográficos
Autor(a) principal: Ferrari,Gláucia Tatiana
Data de Publicação: 2014
Outros Autores: Ozaki,Vitor
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Revista Brasileira de Meteorologia (Online)
Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862014000100003
Resumo: Time series from weather stations in Brazil have several missing data, outliers and spurious zeroes. In order to use this dataset in risk and meteorological studies, one should take into account alternative methodologies to deal with these problems. This article describes the statistical imputation and quality control procedures applied to a database of daily precipitation from meteorological stations located in the State of Parana, Brazil. After imputation, the data went through a process of quality control to identify possible errors, such as: identical precipitation over seven consecutive days and precipitation values that differ significantly from the values in neighboring weather stations. Next, we used the extreme value theory to model agricultural drought, considering the maximum number of consecutive days with precipitation below 7 mm for the period between January and February, in the main soybean agricultural regions in the State of Parana.
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spelling Missing data imputation of climate datasets: implications to modeling extreme drought eventsImputationquality controlprecipitationriskTime series from weather stations in Brazil have several missing data, outliers and spurious zeroes. In order to use this dataset in risk and meteorological studies, one should take into account alternative methodologies to deal with these problems. This article describes the statistical imputation and quality control procedures applied to a database of daily precipitation from meteorological stations located in the State of Parana, Brazil. After imputation, the data went through a process of quality control to identify possible errors, such as: identical precipitation over seven consecutive days and precipitation values that differ significantly from the values in neighboring weather stations. Next, we used the extreme value theory to model agricultural drought, considering the maximum number of consecutive days with precipitation below 7 mm for the period between January and February, in the main soybean agricultural regions in the State of Parana.Sociedade Brasileira de Meteorologia2014-03-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862014000100003Revista Brasileira de Meteorologia v.29 n.1 2014reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/S0102-77862014000100003info:eu-repo/semantics/openAccessFerrari,Gláucia TatianaOzaki,Vitoreng2014-03-28T00:00:00Zoai:scielo:S0102-77862014000100003Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2014-03-28T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false
dc.title.none.fl_str_mv Missing data imputation of climate datasets: implications to modeling extreme drought events
title Missing data imputation of climate datasets: implications to modeling extreme drought events
spellingShingle Missing data imputation of climate datasets: implications to modeling extreme drought events
Ferrari,Gláucia Tatiana
Imputation
quality control
precipitation
risk
title_short Missing data imputation of climate datasets: implications to modeling extreme drought events
title_full Missing data imputation of climate datasets: implications to modeling extreme drought events
title_fullStr Missing data imputation of climate datasets: implications to modeling extreme drought events
title_full_unstemmed Missing data imputation of climate datasets: implications to modeling extreme drought events
title_sort Missing data imputation of climate datasets: implications to modeling extreme drought events
author Ferrari,Gláucia Tatiana
author_facet Ferrari,Gláucia Tatiana
Ozaki,Vitor
author_role author
author2 Ozaki,Vitor
author2_role author
dc.contributor.author.fl_str_mv Ferrari,Gláucia Tatiana
Ozaki,Vitor
dc.subject.por.fl_str_mv Imputation
quality control
precipitation
risk
topic Imputation
quality control
precipitation
risk
description Time series from weather stations in Brazil have several missing data, outliers and spurious zeroes. In order to use this dataset in risk and meteorological studies, one should take into account alternative methodologies to deal with these problems. This article describes the statistical imputation and quality control procedures applied to a database of daily precipitation from meteorological stations located in the State of Parana, Brazil. After imputation, the data went through a process of quality control to identify possible errors, such as: identical precipitation over seven consecutive days and precipitation values that differ significantly from the values in neighboring weather stations. Next, we used the extreme value theory to model agricultural drought, considering the maximum number of consecutive days with precipitation below 7 mm for the period between January and February, in the main soybean agricultural regions in the State of Parana.
publishDate 2014
dc.date.none.fl_str_mv 2014-03-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862014000100003
url http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862014000100003
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 10.1590/S0102-77862014000100003
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv text/html
dc.publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
publisher.none.fl_str_mv Sociedade Brasileira de Meteorologia
dc.source.none.fl_str_mv Revista Brasileira de Meteorologia v.29 n.1 2014
reponame:Revista Brasileira de Meteorologia (Online)
instname:Sociedade Brasileira de Meteorologia (SBMET)
instacron:SBMET
instname_str Sociedade Brasileira de Meteorologia (SBMET)
instacron_str SBMET
institution SBMET
reponame_str Revista Brasileira de Meteorologia (Online)
collection Revista Brasileira de Meteorologia (Online)
repository.name.fl_str_mv Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)
repository.mail.fl_str_mv ||rbmet@rbmet.org.br
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