The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature
Autor(a) principal: | |
---|---|
Data de Publicação: | 2019 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | por |
Título da fonte: | Repositório Institucional da UNESP |
Texto Completo: | http://dx.doi.org/10.26848/rbgf.v12.1.p096-111 http://hdl.handle.net/11449/245848 |
Resumo: | Stochastic processes of spatio-temporal nature consist of phenomenons that are characterized by spatial and temporal variability. Currently, it is one of the great growing areas with diverse applications in environmental, geographic, biological, epidemiological sciences, among others. Certainly, conventional statistical methods are not adequate to modeling self-correlated structures in space and time. In fact, there are still major challenges regarding the computational implementation of the geostatistical methodology for the analysis of space-time processes, with emphasis on the spacetime package of the R program used in this study. Thus, this work aims to apply the geostatistical methodology of covariance functions in order to infer about the maximum air temperature of the State of Minas Gerais from 1996 to 2016, aiming to contribute with challenges such as heating uncontrolled urbanization, scarcity of natural resources, epidemics and natural disasters. Using the data from 61 meteorological stations, the geostatistical space-time analysis was performed, in which the sum-metric covariance model was the most adequate, considering the criterion of the Mean Squared Error. Thus, it was possible to prepare maps of predictions of maximum air temperatures in the state of Minas Gerais through of ordinary kriging, assuming first order stationarity of the evaluated stochastic process. It can be observed that the models of space-time geostatistics have shown to be efficient in the space-time studies of maximum air temperatures. |
id |
UNSP_9445e34c4357ab5d946cb8d17fc4ac18 |
---|---|
oai_identifier_str |
oai:repositorio.unesp.br:11449/245848 |
network_acronym_str |
UNSP |
network_name_str |
Repositório Institucional da UNESP |
repository_id_str |
2946 |
spelling |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air TemperatureO Uso da Geoestatística Espaço-Temporal na Predição da Temperatura Máxima do ArCovarianceOrdinary KrigingSpatial-temporal Data ModelingVariogramStochastic processes of spatio-temporal nature consist of phenomenons that are characterized by spatial and temporal variability. Currently, it is one of the great growing areas with diverse applications in environmental, geographic, biological, epidemiological sciences, among others. Certainly, conventional statistical methods are not adequate to modeling self-correlated structures in space and time. In fact, there are still major challenges regarding the computational implementation of the geostatistical methodology for the analysis of space-time processes, with emphasis on the spacetime package of the R program used in this study. Thus, this work aims to apply the geostatistical methodology of covariance functions in order to infer about the maximum air temperature of the State of Minas Gerais from 1996 to 2016, aiming to contribute with challenges such as heating uncontrolled urbanization, scarcity of natural resources, epidemics and natural disasters. Using the data from 61 meteorological stations, the geostatistical space-time analysis was performed, in which the sum-metric covariance model was the most adequate, considering the criterion of the Mean Squared Error. Thus, it was possible to prepare maps of predictions of maximum air temperatures in the state of Minas Gerais through of ordinary kriging, assuming first order stationarity of the evaluated stochastic process. It can be observed that the models of space-time geostatistics have shown to be efficient in the space-time studies of maximum air temperatures.Universidade Federal de Viçosa (UFV), MGDepartamento de Física Universidade Estadual Paulista (Unesp) Faculdade de Ciências, SPInstituto Federal do Espírito Santos (IFES, ESDepartamento de Física Universidade Estadual Paulista (Unesp) Faculdade de Ciências, SPUniversidade Federal de Viçosa (UFV)Universidade Estadual Paulista (UNESP)Instituto Federal do Espírito Santos (IFESViana, Rosane Soares MoreiraRodrigues, Gérson dos SantosMoreira, Demerval Soares [UNESP]Louzada, João MarcosRosa, Lidiane Maria Ferraz2023-07-29T12:24:50Z2023-07-29T12:24:50Z2019-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/article96-111http://dx.doi.org/10.26848/rbgf.v12.1.p096-111Revista Brasileira de Geografia Fisica, v. 12, n. 1, p. 96-111, 2019.1984-2295http://hdl.handle.net/11449/24584810.26848/rbgf.v12.1.p096-1112-s2.0-85100211042Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPporRevista Brasileira de Geografia Fisicainfo:eu-repo/semantics/openAccess2023-07-29T12:24:50Zoai:repositorio.unesp.br:11449/245848Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462023-07-29T12:24:50Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false |
dc.title.none.fl_str_mv |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature O Uso da Geoestatística Espaço-Temporal na Predição da Temperatura Máxima do Ar |
title |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
spellingShingle |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature Viana, Rosane Soares Moreira Covariance Ordinary Kriging Spatial-temporal Data Modeling Variogram |
title_short |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
title_full |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
title_fullStr |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
title_full_unstemmed |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
title_sort |
The Use of Space-Temporal Geostatistics in the Prediction of Maximum Air Temperature |
author |
Viana, Rosane Soares Moreira |
author_facet |
Viana, Rosane Soares Moreira Rodrigues, Gérson dos Santos Moreira, Demerval Soares [UNESP] Louzada, João Marcos Rosa, Lidiane Maria Ferraz |
author_role |
author |
author2 |
Rodrigues, Gérson dos Santos Moreira, Demerval Soares [UNESP] Louzada, João Marcos Rosa, Lidiane Maria Ferraz |
author2_role |
author author author author |
dc.contributor.none.fl_str_mv |
Universidade Federal de Viçosa (UFV) Universidade Estadual Paulista (UNESP) Instituto Federal do Espírito Santos (IFES |
dc.contributor.author.fl_str_mv |
Viana, Rosane Soares Moreira Rodrigues, Gérson dos Santos Moreira, Demerval Soares [UNESP] Louzada, João Marcos Rosa, Lidiane Maria Ferraz |
dc.subject.por.fl_str_mv |
Covariance Ordinary Kriging Spatial-temporal Data Modeling Variogram |
topic |
Covariance Ordinary Kriging Spatial-temporal Data Modeling Variogram |
description |
Stochastic processes of spatio-temporal nature consist of phenomenons that are characterized by spatial and temporal variability. Currently, it is one of the great growing areas with diverse applications in environmental, geographic, biological, epidemiological sciences, among others. Certainly, conventional statistical methods are not adequate to modeling self-correlated structures in space and time. In fact, there are still major challenges regarding the computational implementation of the geostatistical methodology for the analysis of space-time processes, with emphasis on the spacetime package of the R program used in this study. Thus, this work aims to apply the geostatistical methodology of covariance functions in order to infer about the maximum air temperature of the State of Minas Gerais from 1996 to 2016, aiming to contribute with challenges such as heating uncontrolled urbanization, scarcity of natural resources, epidemics and natural disasters. Using the data from 61 meteorological stations, the geostatistical space-time analysis was performed, in which the sum-metric covariance model was the most adequate, considering the criterion of the Mean Squared Error. Thus, it was possible to prepare maps of predictions of maximum air temperatures in the state of Minas Gerais through of ordinary kriging, assuming first order stationarity of the evaluated stochastic process. It can be observed that the models of space-time geostatistics have shown to be efficient in the space-time studies of maximum air temperatures. |
publishDate |
2019 |
dc.date.none.fl_str_mv |
2019-01-01 2023-07-29T12:24:50Z 2023-07-29T12:24:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://dx.doi.org/10.26848/rbgf.v12.1.p096-111 Revista Brasileira de Geografia Fisica, v. 12, n. 1, p. 96-111, 2019. 1984-2295 http://hdl.handle.net/11449/245848 10.26848/rbgf.v12.1.p096-111 2-s2.0-85100211042 |
url |
http://dx.doi.org/10.26848/rbgf.v12.1.p096-111 http://hdl.handle.net/11449/245848 |
identifier_str_mv |
Revista Brasileira de Geografia Fisica, v. 12, n. 1, p. 96-111, 2019. 1984-2295 10.26848/rbgf.v12.1.p096-111 2-s2.0-85100211042 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.none.fl_str_mv |
Revista Brasileira de Geografia Fisica |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
96-111 |
dc.source.none.fl_str_mv |
Scopus reponame:Repositório Institucional da UNESP instname:Universidade Estadual Paulista (UNESP) instacron:UNESP |
instname_str |
Universidade Estadual Paulista (UNESP) |
instacron_str |
UNESP |
institution |
UNESP |
reponame_str |
Repositório Institucional da UNESP |
collection |
Repositório Institucional da UNESP |
repository.name.fl_str_mv |
Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP) |
repository.mail.fl_str_mv |
|
_version_ |
1799965017728090112 |