Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM
Autor(a) principal: | |
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Data de Publicação: | 2021 |
Outros Autores: | , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Research, Society and Development |
Texto Completo: | https://rsdjournal.org/index.php/rsd/article/view/13616 |
Resumo: | The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted. |
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Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AMEvaluación de distribuciones de probabilidad en el análisis de series de temperaturas mínimas en Manaus - AMAvaliação de distribuições de probabilidade na análise de séries de temperatura mínima em Manaus – AMDistribution adjustmentTemperature dataRice distributionLog-Normal DistributionGumbel II distribution.Ajuste de distribucionesDatos de temperaturaDistribución riceDistribución Log-NormalDistribución de Gumbel II.Ajuste de distribuiçõesDados de temperaturaDistribuição riceDistribuição Log-NormalDistribuição Gumbel II.The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted.La relevancia del estudio de los fenómenos climatológicos se basa en la influencia que tienen en el mundo variables de esta naturaleza. Entre las variables más observadas destaca la temperatura, cuyo efecto de su variación puede ocasionar impactos significativos, como en la proliferación de especies biológicas, producción agrícola, salud de la población, etc. Se han estudiado las distribuciones de probabilidad para verificar el mejor ajuste para describir y/o predecir el comportamiento de las variables climáticas y, en este contexto, el presente estudio evaluó, entre seis distribuciones de probabilidad, el mejor ajuste para describir un promedio mensual mínimo de una serie histórica de temperaturas. La serie utilizada en este estudio cubre un período de 38 años (1980 a 2018) separados por meses, de la estación meteorológica de la estación Manaus - AM (OMM: 82331) obtenida del INMET, totalizando 459 observaciones. Se utilizaron pruebas de signo de diferencia y punto de inflexión para verificar la independencia de los datos y el método de máxima verosimilitud para estimar los parámetros. Para seleccionar la distribución de mejor ajuste, se utilizaron los gráficos de Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion y cuantiles-cuantiles. Se evaluaron las distribuciones Log-Normal, Gama, Weibull, Gumbel tipo II, Benini y Rice, destacándose las distribuciones Rice, Log-Normal y Gumbel II como las de mejor desempeño.A relevância em estudar fenômenos climatológicos baseia-se na influência que variáveis dessa natureza exercem no mundo. Entre as variáveis mais observadas, destaca-se a temperatura, cujo efeito de sua variação pode vir a causar impactos significativos, como na proliferação de espécies biológicas, produção agrícola, saúde da população, etc. Distribuições de probabilidade vêm sendo estudadas para verificar o melhor ajuste para descrever e/ou prever o comportamento de variáveis climáticas e, sob esse contexto, o presente estudo avaliou, dentre seis distribuições de probabilidade, a de melhor ajuste para descrever uma série histórica de temperatura mínima média mensal. As séries utilizadas neste estudo englobam um período de 38 anos (1980 a 2018) separados por mês, da estação meteorológica da estação Manaus - AM (OMM: 82331) obtidas no INMET, totalizando 459 observações. Foram utilizados os testes Difference-Sign e Turning Point para verificar independência dos dados e o método da máxima verossimilhança para estimar os parâmetros. Para selecionar a distribuição de melhor ajuste foram utilizados os testes de Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Critério de Informação de Akaike e gráficos quantil-quantil. Foram avaliadas as distribuições Log-Normal, Gama, Weibull, Gumbel tipo II, Benini e Rice, destacando-se as distribuições Rice, Log-Normal e Gumbel II como as de melhor desempenho.Research, Society and Development2021-03-22info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://rsdjournal.org/index.php/rsd/article/view/1361610.33448/rsd-v10i3.13616Research, Society and Development; Vol. 10 No. 3; e46210313616Research, Society and Development; Vol. 10 Núm. 3; e46210313616Research, Society and Development; v. 10 n. 3; e462103136162525-3409reponame:Research, Society and Developmentinstname:Universidade Federal de Itajubá (UNIFEI)instacron:UNIFEIenghttps://rsdjournal.org/index.php/rsd/article/view/13616/12180Copyright (c) 2021 Yana Miranda Borges; Breno Gabriel da Silva; Brian Alvarez Ribeiro de Melo; Robério Rebouças da Silvahttps://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessBorges, Yana MirandaSilva, Breno Gabriel da Melo, Brian Alvarez Ribeiro de Silva, Robério Rebouças da 2021-03-28T12:03:35Zoai:ojs.pkp.sfu.ca:article/13616Revistahttps://rsdjournal.org/index.php/rsd/indexPUBhttps://rsdjournal.org/index.php/rsd/oairsd.articles@gmail.com2525-34092525-3409opendoar:2024-01-17T09:34:54.823178Research, Society and Development - Universidade Federal de Itajubá (UNIFEI)false |
dc.title.none.fl_str_mv |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM Evaluación de distribuciones de probabilidad en el análisis de series de temperaturas mínimas en Manaus - AM Avaliação de distribuições de probabilidade na análise de séries de temperatura mínima em Manaus – AM |
title |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
spellingShingle |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM Borges, Yana Miranda Distribution adjustment Temperature data Rice distribution Log-Normal Distribution Gumbel II distribution. Ajuste de distribuciones Datos de temperatura Distribución rice Distribución Log-Normal Distribución de Gumbel II. Ajuste de distribuições Dados de temperatura Distribuição rice Distribuição Log-Normal Distribuição Gumbel II. |
title_short |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
title_full |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
title_fullStr |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
title_full_unstemmed |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
title_sort |
Evaluation of probability distributions in the analysis of minimum temperature series in Manaus – AM |
author |
Borges, Yana Miranda |
author_facet |
Borges, Yana Miranda Silva, Breno Gabriel da Melo, Brian Alvarez Ribeiro de Silva, Robério Rebouças da |
author_role |
author |
author2 |
Silva, Breno Gabriel da Melo, Brian Alvarez Ribeiro de Silva, Robério Rebouças da |
author2_role |
author author author |
dc.contributor.author.fl_str_mv |
Borges, Yana Miranda Silva, Breno Gabriel da Melo, Brian Alvarez Ribeiro de Silva, Robério Rebouças da |
dc.subject.por.fl_str_mv |
Distribution adjustment Temperature data Rice distribution Log-Normal Distribution Gumbel II distribution. Ajuste de distribuciones Datos de temperatura Distribución rice Distribución Log-Normal Distribución de Gumbel II. Ajuste de distribuições Dados de temperatura Distribuição rice Distribuição Log-Normal Distribuição Gumbel II. |
topic |
Distribution adjustment Temperature data Rice distribution Log-Normal Distribution Gumbel II distribution. Ajuste de distribuciones Datos de temperatura Distribución rice Distribución Log-Normal Distribución de Gumbel II. Ajuste de distribuições Dados de temperatura Distribuição rice Distribuição Log-Normal Distribuição Gumbel II. |
description |
The relevance in studying climatological phenomena is based on the influence that variables of this nature exert on the world. Among the most observed variables, temperature stands out, whose effect of its variation may cause significant impacts, such as the proliferation of biological species, agricultural production, population health, etc. Probability distributions have been studied to verify the best fit to describe and/or predict the behavior of climate variables and, in this context, the present study evaluated, among six probability distributions, the best fit to describe a historical temperature series. minimum monthly mean. The series used in this study encompass a period of 38 years (1980 to 2018) separated by month from the weather station of the Manaus - AM station (OMM: 82331) obtained from INMET, totaling 459 observations. Difference-Sign and Turning Point tests were used to verify data independence and the maximum likelihood method to estimate the parameters. Kolmogorov-Smirnov, Anderson-Darling, Cramér-von Mises, Akaike Information Criterion and quantile-quantile plots were used to select the best fit distribution. Log-Normal, Gama, Weibull, Gumbel type II, Benini and Rice distributions were evaluated, with the best performing Rice, Log-Normal and Gumbel II distributions being highlighted. |
publishDate |
2021 |
dc.date.none.fl_str_mv |
2021-03-22 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/13616 10.33448/rsd-v10i3.13616 |
url |
https://rsdjournal.org/index.php/rsd/article/view/13616 |
identifier_str_mv |
10.33448/rsd-v10i3.13616 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
https://rsdjournal.org/index.php/rsd/article/view/13616/12180 |
dc.rights.driver.fl_str_mv |
https://creativecommons.org/licenses/by/4.0 info:eu-repo/semantics/openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by/4.0 |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Research, Society and Development |
publisher.none.fl_str_mv |
Research, Society and Development |
dc.source.none.fl_str_mv |
Research, Society and Development; Vol. 10 No. 3; e46210313616 Research, Society and Development; Vol. 10 Núm. 3; e46210313616 Research, Society and Development; v. 10 n. 3; e46210313616 2525-3409 reponame:Research, Society and Development instname:Universidade Federal de Itajubá (UNIFEI) instacron:UNIFEI |
instname_str |
Universidade Federal de Itajubá (UNIFEI) |
instacron_str |
UNIFEI |
institution |
UNIFEI |
reponame_str |
Research, Society and Development |
collection |
Research, Society and Development |
repository.name.fl_str_mv |
Research, Society and Development - Universidade Federal de Itajubá (UNIFEI) |
repository.mail.fl_str_mv |
rsd.articles@gmail.com |
_version_ |
1797052747042783232 |