Análise das propriedades multifractais das séries climáticas no Brasil
Autor(a) principal: | |
---|---|
Data de Publicação: | 2018 |
Tipo de documento: | Tese |
Idioma: | por |
Título da fonte: | Biblioteca Digital de Teses e Dissertações da UFRPE |
Texto Completo: | http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7778 |
Resumo: | Research on the dynamics of climate variables can provide important information about their spatio-temporal variability. The understanding of these processes is fundamental for the development of predictive climate models. Climatic variables depend on a variety of natural processes and show random fluctuations at different time and space scales, and linear statistical methods are insufficient for a full time series analysis. Thus, in order to understand the temporal complexity of climatic elements (air temperature (maximum, minimum, average), mean air humidity and mean wind velocity), we used the multifractal detrended fluctuation analysis (MF-DFA) to analyze the multifractal properties of historical series of these variables, provided by the National Institute of Meteorology (INMET), for 265 meteorological stations distributed in Brazil. The results indicate that the process that generates the variability of these climatic variables follows the multifractal dynamics, with greater influence of the seasonal component on the series. It was observed a persistent autocorrelation only in the variables average, maximum and minimum temperature, and this persistence is stronger in the proximity of the line of the Equator. The degree of multifractality indicated by the width of the multifractal spectrum varies between regions, since, for the variables average and minimum temperature, a spatial pattern was observed in which the regions near the equator line presented a lower degree. Regarding the parameter of asymmetry, a homogeneous spatial distribution was observed, and for all variables studied multifractality is more influenced by the small fluctuations, indicating that the temporal component with the greatest influence on these variables was the seasonality. In addition, we investigated the cause of multifractality by analyzing the randomized series. For most meteorological variables studied, multifractality is due to the probability density function and/or to the joint action of the probability density function and long-range correlations. The results indicate that the modeling of long memory in time series of climatic variables should be done through a multifractal model and may contribute to the development of more reliable predictive models. |
id |
URPE_08fcd4056ba6b4cacd99aaea6c3dd898 |
---|---|
oai_identifier_str |
oai:tede2:tede2/7778 |
network_acronym_str |
URPE |
network_name_str |
Biblioteca Digital de Teses e Dissertações da UFRPE |
repository_id_str |
|
spelling |
STOSIC, TatijanaSTOSIC, BorkoARAÚJO, Lázaro de SoutoBEJAN, Lucian BogdanCUNHA FILHO, MoacyrSTOSIC, Borkohttp://lattes.cnpq.br/8757276796229100SILVA, Hérica Santos da2018-12-14T12:45:05Z2018-10-11SILVA, Hérica Santos da. Análise das propriedades multifractais das séries climáticas no Brasil. 2018. 109 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife.http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7778Research on the dynamics of climate variables can provide important information about their spatio-temporal variability. The understanding of these processes is fundamental for the development of predictive climate models. Climatic variables depend on a variety of natural processes and show random fluctuations at different time and space scales, and linear statistical methods are insufficient for a full time series analysis. Thus, in order to understand the temporal complexity of climatic elements (air temperature (maximum, minimum, average), mean air humidity and mean wind velocity), we used the multifractal detrended fluctuation analysis (MF-DFA) to analyze the multifractal properties of historical series of these variables, provided by the National Institute of Meteorology (INMET), for 265 meteorological stations distributed in Brazil. The results indicate that the process that generates the variability of these climatic variables follows the multifractal dynamics, with greater influence of the seasonal component on the series. It was observed a persistent autocorrelation only in the variables average, maximum and minimum temperature, and this persistence is stronger in the proximity of the line of the Equator. The degree of multifractality indicated by the width of the multifractal spectrum varies between regions, since, for the variables average and minimum temperature, a spatial pattern was observed in which the regions near the equator line presented a lower degree. Regarding the parameter of asymmetry, a homogeneous spatial distribution was observed, and for all variables studied multifractality is more influenced by the small fluctuations, indicating that the temporal component with the greatest influence on these variables was the seasonality. In addition, we investigated the cause of multifractality by analyzing the randomized series. For most meteorological variables studied, multifractality is due to the probability density function and/or to the joint action of the probability density function and long-range correlations. The results indicate that the modeling of long memory in time series of climatic variables should be done through a multifractal model and may contribute to the development of more reliable predictive models.A investigação da dinâmica de variáveis climáticas pode fornecer informações importantes sobre a sua variabilidade espaço-temporal. O entendimento sobre esses processos é fundamental para o desenvolvimento de modelos preditivos climáticos. As variáveis climáticas dependem de uma diversidade de processos naturais e mostram flutuações aleatórias em diferentes escalas temporais e espaciais, e os métodos estatísticos lineares são insuficientes para uma análise completa de séries temporais. Desta forma, a fim de compreender a complexidade temporal dos elementos climáticos (temperatura do ar (máxima, mínima, média), umidade relativa média do ar e velocidade média do vento) no Brasil, utilizou-se a análise multifractal das flutuações destendenciada (Multifractal detrended fluctuation analysis - MF-DFA) para analisar as propriedades multifractais de séries históricas dessas variáveis, fornecidas pelo Instituto Nacional de Meteorologia (INMET), em 265 estações meteorológicas distribuídas no Brasil. Os resultados apontam que o processo que gera a variabilidade dessas variáveis climáticas segue a dinâmica multifractal, com maior influência do componente sazonal sobre as séries. Observou-se uma autocorrelação persistente apenas nas variáveis temperatura média, máxima e mínima, e essa persistência é mais forte na proximidade da linha do Equador. O grau de multifractalidade indicado pela largura do espectro multifractal varia entre as regiões, visto que, para as variáveis temperatura média e mínima, observou-se um padrão espacial no qual as regiões próximas da linha do Equador apresentaram um menor grau. Já em relação ao parâmetro de assimetria, verificou-se uma distribuição espacial homogênea, em que todas as variáveis estudadas a multifractalidade é mais influenciada pelas pequenas flutuações. Além disso, investigou-se a causa de multifractalidade, analisando as séries randomizadas. Para a maioria das variáveis meteorológicas estudadas, a multifractalidade deve-se à função de densidade de probabilidade e/ou à ação conjunta da função de densidade de probabilidade e de correlações de longo alcance. Os resultados observados indicam que a modelagem da memória longa em séries temporais de variáveis climáticas deveria ser feita por meio de um modelo multifractal e pode contribuir para o desenvolvimento de modelos preditivos mais confiáveis.Submitted by Mario BC (mario@bc.ufrpe.br) on 2018-12-14T12:45:05Z No. of bitstreams: 1 Herica Santos da Silva.pdf: 3052477 bytes, checksum: f0a84627ffe37c72c9799ed881c912ec (MD5)Made available in DSpace on 2018-12-14T12:45:05Z (GMT). No. of bitstreams: 1 Herica Santos da Silva.pdf: 3052477 bytes, checksum: f0a84627ffe37c72c9799ed881c912ec (MD5) Previous issue date: 2018-10-11Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPESapplication/pdfporUniversidade Federal Rural de PernambucoPrograma de Pós-Graduação em Biometria e Estatística AplicadaUFRPEBrasilDepartamento de Estatística e InformáticaAnálise multifractalVariável climáticaSérie temporalCIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICAAnálise das propriedades multifractais das séries climáticas no Brasilinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesis768382242446187918600600600600-6774555140396120501-58364078281851435172075167498588264571info:eu-repo/semantics/openAccessreponame:Biblioteca Digital de Teses e Dissertações da UFRPEinstname:Universidade Federal Rural de Pernambuco (UFRPE)instacron:UFRPEORIGINALHerica Santos da Silva.pdfHerica Santos da Silva.pdfapplication/pdf3052477http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7778/2/Herica+Santos+da+Silva.pdff0a84627ffe37c72c9799ed881c912ecMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-82165http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7778/1/license.txtbd3efa91386c1718a7f26a329fdcb468MD51tede2/77782018-12-14 09:45:05.215oai:tede2: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Biblioteca Digital de Teses e Dissertaçõeshttp://www.tede2.ufrpe.br:8080/tede/PUBhttp://www.tede2.ufrpe.br:8080/oai/requestbdtd@ufrpe.br ||bdtd@ufrpe.bropendoar:2018-12-14T12:45:05Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE)false |
dc.title.por.fl_str_mv |
Análise das propriedades multifractais das séries climáticas no Brasil |
title |
Análise das propriedades multifractais das séries climáticas no Brasil |
spellingShingle |
Análise das propriedades multifractais das séries climáticas no Brasil SILVA, Hérica Santos da Análise multifractal Variável climática Série temporal CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
title_short |
Análise das propriedades multifractais das séries climáticas no Brasil |
title_full |
Análise das propriedades multifractais das séries climáticas no Brasil |
title_fullStr |
Análise das propriedades multifractais das séries climáticas no Brasil |
title_full_unstemmed |
Análise das propriedades multifractais das séries climáticas no Brasil |
title_sort |
Análise das propriedades multifractais das séries climáticas no Brasil |
author |
SILVA, Hérica Santos da |
author_facet |
SILVA, Hérica Santos da |
author_role |
author |
dc.contributor.advisor1.fl_str_mv |
STOSIC, Tatijana |
dc.contributor.advisor-co1.fl_str_mv |
STOSIC, Borko |
dc.contributor.referee1.fl_str_mv |
ARAÚJO, Lázaro de Souto |
dc.contributor.referee2.fl_str_mv |
BEJAN, Lucian Bogdan |
dc.contributor.referee3.fl_str_mv |
CUNHA FILHO, Moacyr |
dc.contributor.referee4.fl_str_mv |
STOSIC, Borko |
dc.contributor.authorLattes.fl_str_mv |
http://lattes.cnpq.br/8757276796229100 |
dc.contributor.author.fl_str_mv |
SILVA, Hérica Santos da |
contributor_str_mv |
STOSIC, Tatijana STOSIC, Borko ARAÚJO, Lázaro de Souto BEJAN, Lucian Bogdan CUNHA FILHO, Moacyr STOSIC, Borko |
dc.subject.por.fl_str_mv |
Análise multifractal Variável climática Série temporal |
topic |
Análise multifractal Variável climática Série temporal CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
dc.subject.cnpq.fl_str_mv |
CIENCIAS EXATAS E DA TERRA::PROBABILIDADE E ESTATISTICA |
description |
Research on the dynamics of climate variables can provide important information about their spatio-temporal variability. The understanding of these processes is fundamental for the development of predictive climate models. Climatic variables depend on a variety of natural processes and show random fluctuations at different time and space scales, and linear statistical methods are insufficient for a full time series analysis. Thus, in order to understand the temporal complexity of climatic elements (air temperature (maximum, minimum, average), mean air humidity and mean wind velocity), we used the multifractal detrended fluctuation analysis (MF-DFA) to analyze the multifractal properties of historical series of these variables, provided by the National Institute of Meteorology (INMET), for 265 meteorological stations distributed in Brazil. The results indicate that the process that generates the variability of these climatic variables follows the multifractal dynamics, with greater influence of the seasonal component on the series. It was observed a persistent autocorrelation only in the variables average, maximum and minimum temperature, and this persistence is stronger in the proximity of the line of the Equator. The degree of multifractality indicated by the width of the multifractal spectrum varies between regions, since, for the variables average and minimum temperature, a spatial pattern was observed in which the regions near the equator line presented a lower degree. Regarding the parameter of asymmetry, a homogeneous spatial distribution was observed, and for all variables studied multifractality is more influenced by the small fluctuations, indicating that the temporal component with the greatest influence on these variables was the seasonality. In addition, we investigated the cause of multifractality by analyzing the randomized series. For most meteorological variables studied, multifractality is due to the probability density function and/or to the joint action of the probability density function and long-range correlations. The results indicate that the modeling of long memory in time series of climatic variables should be done through a multifractal model and may contribute to the development of more reliable predictive models. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-12-14T12:45:05Z |
dc.date.issued.fl_str_mv |
2018-10-11 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/doctoralThesis |
format |
doctoralThesis |
status_str |
publishedVersion |
dc.identifier.citation.fl_str_mv |
SILVA, Hérica Santos da. Análise das propriedades multifractais das séries climáticas no Brasil. 2018. 109 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
dc.identifier.uri.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7778 |
identifier_str_mv |
SILVA, Hérica Santos da. Análise das propriedades multifractais das séries climáticas no Brasil. 2018. 109 f. Tese (Programa de Pós-Graduação em Biometria e Estatística Aplicada) - Universidade Federal Rural de Pernambuco, Recife. |
url |
http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/7778 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.relation.program.fl_str_mv |
768382242446187918 |
dc.relation.confidence.fl_str_mv |
600 600 600 600 |
dc.relation.department.fl_str_mv |
-6774555140396120501 |
dc.relation.cnpq.fl_str_mv |
-5836407828185143517 |
dc.relation.sponsorship.fl_str_mv |
2075167498588264571 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Universidade Federal Rural de Pernambuco |
dc.publisher.program.fl_str_mv |
Programa de Pós-Graduação em Biometria e Estatística Aplicada |
dc.publisher.initials.fl_str_mv |
UFRPE |
dc.publisher.country.fl_str_mv |
Brasil |
dc.publisher.department.fl_str_mv |
Departamento de Estatística e Informática |
publisher.none.fl_str_mv |
Universidade Federal Rural de Pernambuco |
dc.source.none.fl_str_mv |
reponame:Biblioteca Digital de Teses e Dissertações da UFRPE instname:Universidade Federal Rural de Pernambuco (UFRPE) instacron:UFRPE |
instname_str |
Universidade Federal Rural de Pernambuco (UFRPE) |
instacron_str |
UFRPE |
institution |
UFRPE |
reponame_str |
Biblioteca Digital de Teses e Dissertações da UFRPE |
collection |
Biblioteca Digital de Teses e Dissertações da UFRPE |
bitstream.url.fl_str_mv |
http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7778/2/Herica+Santos+da+Silva.pdf http://www.tede2.ufrpe.br:8080/tede2/bitstream/tede2/7778/1/license.txt |
bitstream.checksum.fl_str_mv |
f0a84627ffe37c72c9799ed881c912ec bd3efa91386c1718a7f26a329fdcb468 |
bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 |
repository.name.fl_str_mv |
Biblioteca Digital de Teses e Dissertações da UFRPE - Universidade Federal Rural de Pernambuco (UFRPE) |
repository.mail.fl_str_mv |
bdtd@ufrpe.br ||bdtd@ufrpe.br |
_version_ |
1800311488283410432 |