Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais

Detalhes bibliográficos
Autor(a) principal: Santos, Roziane Sobreira dos
Data de Publicação: 2012
Tipo de documento: Tese
Idioma: por
Título da fonte: LOCUS Repositório Institucional da UFV
Texto Completo: http://locus.ufv.br/handle/123456789/1510
Resumo: Climatic data is extremely important in the various activities undertaken by man, providing much information concerning the atmospheric environment and the impacts on it. Therefore there is a need for reliable weather information, because a failure in the time series can compromise analysis and data interpretation. To be used safely, weather data must be statistically homogeneous, since the non-homogeneity of a series leads to an erroneous interpretation of the weather conditions to be studied. The lack of historical weather data is significant, both due to the lack of weather stations and the existing gaps, as the available records are not always continuous for long periods of observations. This work aimed to analyze methodologies to evaluate the homogeneity and trend, as well as gap filling data series of maximum and minimum temperatures of air and rainfall in Minas Gerais. The homogeneity analysis was performed by three tests for the identification of discontinuity points in time series: Standard Normal Homogeneity Test (SNHT) to a single point test, Pettitt test and Buishand test. In trend analysis the Mann-Kendall and regression analysis were used. To fill gaps, two methodologies were used: the SARIMA models proposed by Box-Jenkins that uses the station's own data with gaps, and a space-time geostatistical model that considers the neighboring stations and the series with gaps, considering both the spatial and temporal dependence of the data. In the homogeneity analysis, 73% of maximum air temperature series and 71% of minimum air temperature series were considered homogeneous. The statistically significant changes occurred in the 1990s, especially around 1997. Most trends are increasing, especially in minimum air temperature. The precipitation series showed no significant trends. The SARIMA models capture well the behavior pattern of the series, with r2 values among 0.47 to 0.68 in maximum temperature and 0.85 to 0.90 for minimum temperature. The errors associated with the models were low, with a root mean square error of about 1 ° C and bias of around 0.4 º C. The space-time geostatistical model showed better results than the Box-Jenkins methodology, being more efficient, with high values of r2 and the Willmott‟s index of agreement and mean errors very close to zero. Even in a precipitation series the forecasts were well adjusted. The lowest value of r2 found explains 70% of the precipitation data variability. For maximum temperature the smallest value of r2 is 82%, much higher than the Box-Jenkins‟ model. Comparing the two methods, the space-time is model are closer to the identity, presenting a lower dispersion around the estimated straight line and coefficient of determination greater.
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spelling Santos, Roziane Sobreira doshttp://lattes.cnpq.br/4983021820079917Pruski, Fernando Falcohttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4727304E8Sediyama, Gilberto Chohakuhttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788051E6Silva, Welliam Chaves Monteiro dahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4707320P6Costa, José Maria Nogueira dahttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783772Y3Leal, Brauliro Gonçalveshttp://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4784843E52015-03-26T12:49:17Z2012-11-192015-03-26T12:49:17Z2012-02-24SANTOS, Roziane Sobreira dos. Homogeneity and climatological series reconstruction for some localities in the state of Minas Gerais. 2012. 100 f. Tese (Doutorado em Agrometeorologia; Climatologia; Micrometeorologia) - Universidade Federal de Viçosa, Viçosa, 2012.http://locus.ufv.br/handle/123456789/1510Climatic data is extremely important in the various activities undertaken by man, providing much information concerning the atmospheric environment and the impacts on it. Therefore there is a need for reliable weather information, because a failure in the time series can compromise analysis and data interpretation. To be used safely, weather data must be statistically homogeneous, since the non-homogeneity of a series leads to an erroneous interpretation of the weather conditions to be studied. The lack of historical weather data is significant, both due to the lack of weather stations and the existing gaps, as the available records are not always continuous for long periods of observations. This work aimed to analyze methodologies to evaluate the homogeneity and trend, as well as gap filling data series of maximum and minimum temperatures of air and rainfall in Minas Gerais. The homogeneity analysis was performed by three tests for the identification of discontinuity points in time series: Standard Normal Homogeneity Test (SNHT) to a single point test, Pettitt test and Buishand test. In trend analysis the Mann-Kendall and regression analysis were used. To fill gaps, two methodologies were used: the SARIMA models proposed by Box-Jenkins that uses the station's own data with gaps, and a space-time geostatistical model that considers the neighboring stations and the series with gaps, considering both the spatial and temporal dependence of the data. In the homogeneity analysis, 73% of maximum air temperature series and 71% of minimum air temperature series were considered homogeneous. The statistically significant changes occurred in the 1990s, especially around 1997. Most trends are increasing, especially in minimum air temperature. The precipitation series showed no significant trends. The SARIMA models capture well the behavior pattern of the series, with r2 values among 0.47 to 0.68 in maximum temperature and 0.85 to 0.90 for minimum temperature. The errors associated with the models were low, with a root mean square error of about 1 ° C and bias of around 0.4 º C. The space-time geostatistical model showed better results than the Box-Jenkins methodology, being more efficient, with high values of r2 and the Willmott‟s index of agreement and mean errors very close to zero. Even in a precipitation series the forecasts were well adjusted. The lowest value of r2 found explains 70% of the precipitation data variability. For maximum temperature the smallest value of r2 is 82%, much higher than the Box-Jenkins‟ model. Comparing the two methods, the space-time is model are closer to the identity, presenting a lower dispersion around the estimated straight line and coefficient of determination greater.Os dados climáticos são de extrema importância nas diversas atividades realizadas pelo homem fornecendo informações importantes do ambiente atmosférico e os impactos que ocorrem nele. Portanto há necessidade de informações meteorológicas confiáveis, pois uma falha na série temporal pode comprometer a análise e a interpretação dos dados. Os dados climáticos, para serem usados com segurança, devem ser estatisticamente homogêneos, uma vez que a não-homogeneidade de uma série temporal conduz a uma interpretação errônea das condições do clima a ser estudado. A falta de dados meteorológicos nas séries de dados históricos para as condições brasileiras é expressiva, tanto pela baixa densidade de estações, como pelas falhas existentes, pois nem sempre os registros disponíveis são contínuos para longos períodos de observações. Com esse trabalho objetivou-se: analisar metodologias para avaliar a homogeneidade, a tendência e o preenchimento de falhas em séries de dados de temperatura máxima e mínima do ar e da precipitação em Minas Gerais. A análise de homogeneidade foi feita por meio de três testes de identificação de pontos de descontinuidade: teste de Homogeneidade Normal Padrão (SNHT) para um único ponto, teste de Pettitt e teste de Buishand. Na análise de tendência foram utilizados os testes de Mann-Kendall e análise de regressão. Para preenchimento de falhas utilizou-se duas metodologias: os modelos SARIMA proposto por Box-Jenkins, que utilizam os dados da própria estação com dados faltantes, e o modelo geoestatístico espaço-tempo, que considera as estações vizinhas, ponderando tanto a dependência espacial como temporal dos dados. Na análise de homogeneidade, 73% das séries de temperatura máxima do ar e 71% das séries de temperatura mínima do ar, foram consideradas homogêneas. As mudanças estatisticamente significativas ocorreram na década de 1990, principalmente em torno do ano de 1997. A maior parte das tendências observadas é de aumento, especialmente na temperatura mínima do ar. As séries de precipitação não apresentaram tendências significativas. Os modelos SARIMA capturam bem o comportamento padrão das séries temporais, com valores de r2 variando de 0,47 a 0,68 para temperatura máxima e de 0,85 a 0,90 para temperatura mínima. Os erros associados pelos modelos foram baixos, com raiz do erro quadrático médio em torno de 1ºC e viés médio próximo de 0,4ºC. O modelo geoestatístico espaço-tempo apresentou resultados melhores do que a metodologia de Box-Jenkins, sendo mais eficiente, com altos valores de r2 e do índice de concordância de Willmott e erros médios bem próximos de zero. Inclusive em séries de precipitação as previsões foram bem ajustadas. O menor valor de r2 encontrado explica 70% da variabilidade dos dados de precipitação. Para a temperatura máxima o menor valor de r2 foi de 82%, muito superior ao modelo de Box-Jenkins. Comparando as duas metodologias, o modelo espaço-temporal está mais próximo da identidade, apresentando menor dispersão em torno da reta estimada e maior coeficiente de determinação.Coordenação de Aperfeiçoamento de Pessoal de Nível Superiorapplication/pdfporUniversidade Federal de ViçosaDoutorado em Meteorologia AgrícolaUFVBRAgrometeorologia; Climatologia; MicrometeorologiaClimatologiaMinas GeraisClimatologia agrícolaClimatologyMinas GeraisAgricultural climatologyCNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIAHomogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas GeraisHomogeneity and climatological series reconstruction for some localities in the state of Minas Geraisinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisinfo:eu-repo/semantics/openAccessreponame:LOCUS Repositório Institucional da UFVinstname:Universidade Federal de Viçosa (UFV)instacron:UFVORIGINALtexto completo.pdfapplication/pdf1487588https://locus.ufv.br//bitstream/123456789/1510/1/texto%20completo.pdf54526cc0d1531054806a28bc811bbf0eMD51TEXTtexto completo.pdf.txttexto completo.pdf.txtExtracted texttext/plain159661https://locus.ufv.br//bitstream/123456789/1510/2/texto%20completo.pdf.txt192b23544ddd2b0bd640027d85858a77MD52THUMBNAILtexto completo.pdf.jpgtexto completo.pdf.jpgIM Thumbnailimage/jpeg3555https://locus.ufv.br//bitstream/123456789/1510/3/texto%20completo.pdf.jpg4ba1f3c239eacb742653e124bb3d7349MD53123456789/15102016-04-07 23:09:28.872oai:locus.ufv.br:123456789/1510Repositório InstitucionalPUBhttps://www.locus.ufv.br/oai/requestfabiojreis@ufv.bropendoar:21452016-04-08T02:09:28LOCUS Repositório Institucional da UFV - Universidade Federal de Viçosa (UFV)false
dc.title.por.fl_str_mv Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
dc.title.alternative.eng.fl_str_mv Homogeneity and climatological series reconstruction for some localities in the state of Minas Gerais
title Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
spellingShingle Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
Santos, Roziane Sobreira dos
Climatologia
Minas Gerais
Climatologia agrícola
Climatology
Minas Gerais
Agricultural climatology
CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA
title_short Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
title_full Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
title_fullStr Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
title_full_unstemmed Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
title_sort Homogeneidade e reconstrução de séries climatológicas para localidades no estado de Minas Gerais
author Santos, Roziane Sobreira dos
author_facet Santos, Roziane Sobreira dos
author_role author
dc.contributor.authorLattes.por.fl_str_mv http://lattes.cnpq.br/4983021820079917
dc.contributor.author.fl_str_mv Santos, Roziane Sobreira dos
dc.contributor.advisor-co1.fl_str_mv Pruski, Fernando Falco
dc.contributor.advisor-co1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4727304E8
dc.contributor.advisor1.fl_str_mv Sediyama, Gilberto Chohaku
dc.contributor.advisor1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4788051E6
dc.contributor.referee1.fl_str_mv Silva, Welliam Chaves Monteiro da
dc.contributor.referee1Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4707320P6
dc.contributor.referee2.fl_str_mv Costa, José Maria Nogueira da
dc.contributor.referee2Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4783772Y3
dc.contributor.referee3.fl_str_mv Leal, Brauliro Gonçalves
dc.contributor.referee3Lattes.fl_str_mv http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4784843E5
contributor_str_mv Pruski, Fernando Falco
Sediyama, Gilberto Chohaku
Silva, Welliam Chaves Monteiro da
Costa, José Maria Nogueira da
Leal, Brauliro Gonçalves
dc.subject.por.fl_str_mv Climatologia
Minas Gerais
Climatologia agrícola
topic Climatologia
Minas Gerais
Climatologia agrícola
Climatology
Minas Gerais
Agricultural climatology
CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA
dc.subject.eng.fl_str_mv Climatology
Minas Gerais
Agricultural climatology
dc.subject.cnpq.fl_str_mv CNPQ::CIENCIAS EXATAS E DA TERRA::GEOCIENCIAS::METEOROLOGIA::CLIMATOLOGIA
description Climatic data is extremely important in the various activities undertaken by man, providing much information concerning the atmospheric environment and the impacts on it. Therefore there is a need for reliable weather information, because a failure in the time series can compromise analysis and data interpretation. To be used safely, weather data must be statistically homogeneous, since the non-homogeneity of a series leads to an erroneous interpretation of the weather conditions to be studied. The lack of historical weather data is significant, both due to the lack of weather stations and the existing gaps, as the available records are not always continuous for long periods of observations. This work aimed to analyze methodologies to evaluate the homogeneity and trend, as well as gap filling data series of maximum and minimum temperatures of air and rainfall in Minas Gerais. The homogeneity analysis was performed by three tests for the identification of discontinuity points in time series: Standard Normal Homogeneity Test (SNHT) to a single point test, Pettitt test and Buishand test. In trend analysis the Mann-Kendall and regression analysis were used. To fill gaps, two methodologies were used: the SARIMA models proposed by Box-Jenkins that uses the station's own data with gaps, and a space-time geostatistical model that considers the neighboring stations and the series with gaps, considering both the spatial and temporal dependence of the data. In the homogeneity analysis, 73% of maximum air temperature series and 71% of minimum air temperature series were considered homogeneous. The statistically significant changes occurred in the 1990s, especially around 1997. Most trends are increasing, especially in minimum air temperature. The precipitation series showed no significant trends. The SARIMA models capture well the behavior pattern of the series, with r2 values among 0.47 to 0.68 in maximum temperature and 0.85 to 0.90 for minimum temperature. The errors associated with the models were low, with a root mean square error of about 1 ° C and bias of around 0.4 º C. The space-time geostatistical model showed better results than the Box-Jenkins methodology, being more efficient, with high values of r2 and the Willmott‟s index of agreement and mean errors very close to zero. Even in a precipitation series the forecasts were well adjusted. The lowest value of r2 found explains 70% of the precipitation data variability. For maximum temperature the smallest value of r2 is 82%, much higher than the Box-Jenkins‟ model. Comparing the two methods, the space-time is model are closer to the identity, presenting a lower dispersion around the estimated straight line and coefficient of determination greater.
publishDate 2012
dc.date.available.fl_str_mv 2012-11-19
2015-03-26T12:49:17Z
dc.date.issued.fl_str_mv 2012-02-24
dc.date.accessioned.fl_str_mv 2015-03-26T12:49:17Z
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dc.identifier.citation.fl_str_mv SANTOS, Roziane Sobreira dos. Homogeneity and climatological series reconstruction for some localities in the state of Minas Gerais. 2012. 100 f. Tese (Doutorado em Agrometeorologia; Climatologia; Micrometeorologia) - Universidade Federal de Viçosa, Viçosa, 2012.
dc.identifier.uri.fl_str_mv http://locus.ufv.br/handle/123456789/1510
identifier_str_mv SANTOS, Roziane Sobreira dos. Homogeneity and climatological series reconstruction for some localities in the state of Minas Gerais. 2012. 100 f. Tese (Doutorado em Agrometeorologia; Climatologia; Micrometeorologia) - Universidade Federal de Viçosa, Viçosa, 2012.
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dc.publisher.department.fl_str_mv Agrometeorologia; Climatologia; Micrometeorologia
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