Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones
Autor(a) principal: | |
---|---|
Data de Publicação: | 2016 |
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=S0102-77862016000200196 |
Resumo: | Abstract Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio-temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio-temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio-temporal model proved to be a versatile technique, adapting to different seasons and dates. |
id |
SBMET-1_71ff82991afa23c503f4995e203a6857 |
---|---|
oai_identifier_str |
oai:scielo:S0102-77862016000200196 |
network_acronym_str |
SBMET-1 |
network_name_str |
Revista Brasileira de Meteorologia (Online) |
repository_id_str |
|
spelling |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zonesspatio-temporal modelrainfall dataordinary krigingordinary cokriginghomogeneous zonesAbstract Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio-temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio-temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio-temporal model proved to be a versatile technique, adapting to different seasons and dates.Sociedade Brasileira de Meteorologia2016-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862016000200196Revista Brasileira de Meteorologia v.31 n.2 2016reponame:Revista Brasileira de Meteorologia (Online)instname:Sociedade Brasileira de Meteorologia (SBMET)instacron:SBMET10.1590/0102-778631220150025info:eu-repo/semantics/openAccessCarvalho,José Ruy Porto DeNakai,Alan MassaruMonteiro,José Eduardo B.A.eng2016-06-28T00:00:00Zoai:scielo:S0102-77862016000200196Revistahttp://www.rbmet.org.br/port/index.phpONGhttps://old.scielo.br/oai/scielo-oai.php||rbmet@rbmet.org.br1982-43510102-7786opendoar:2016-06-28T00:00Revista Brasileira de Meteorologia (Online) - Sociedade Brasileira de Meteorologia (SBMET)false |
dc.title.none.fl_str_mv |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
title |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
spellingShingle |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones Carvalho,José Ruy Porto De spatio-temporal model rainfall data ordinary kriging ordinary cokriging homogeneous zones |
title_short |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
title_full |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
title_fullStr |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
title_full_unstemmed |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
title_sort |
Spatio-Temporal Modeling of Data Imputation for Daily Rainfall Series in Homogeneous Zones |
author |
Carvalho,José Ruy Porto De |
author_facet |
Carvalho,José Ruy Porto De Nakai,Alan Massaru Monteiro,José Eduardo B.A. |
author_role |
author |
author2 |
Nakai,Alan Massaru Monteiro,José Eduardo B.A. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Carvalho,José Ruy Porto De Nakai,Alan Massaru Monteiro,José Eduardo B.A. |
dc.subject.por.fl_str_mv |
spatio-temporal model rainfall data ordinary kriging ordinary cokriging homogeneous zones |
topic |
spatio-temporal model rainfall data ordinary kriging ordinary cokriging homogeneous zones |
description |
Abstract Spatio-temporal modelling is an area of increasing importance in which models and methods have often been developed to deal with specific applications. In this study, a spatio-temporal model was used to estimate daily rainfall data. Rainfall records from several weather stations, obtained from the Agritempo system for two climatic homogeneous zones, were used. Rainfall values obtained for two fixed dates (January 1 and May 1, 2012) using the spatio-temporal model were compared with the geostatisticals techniques of ordinary kriging and ordinary cokriging with altitude as auxiliary variable. The spatio-temporal model was more than 17% better at producing estimates of daily precipitation compared to kriging and cokriging in the first zone and more than 18% in the second zone. The spatio-temporal model proved to be a versatile technique, adapting to different seasons and dates. |
publishDate |
2016 |
dc.date.none.fl_str_mv |
2016-06-01 |
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=S0102-77862016000200196 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0102-77862016000200196 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0102-778631220150025 |
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.31 n.2 2016 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_ |
1752122085306531840 |