Missing data imputation of climate datasets: implications to modeling extreme drought events
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | |
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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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 |
_version_ |
1752122084760223744 |