Estudo Pluvial no Nordeste do Brasil Utilizando Análise Multivariada (Rain Study in Northeast Brazil Using Multivariate Analysis)
DOI:
https://doi.org/10.26848/rbgf.v5i3.232781Keywords:
Componentes principais, agrupamento, índices pluviais.Abstract
O objetivo deste trabalho foi estudar a dinâmica de variabilidade climática espacial e temporal da pluviosidade nos nove estados do Nordeste Brasileiro, utilizando as técnicas multivariadas de Análise de Componentes Principais (ACP) e Análise de Agrupamento (AA). Foram utilizadas médias mensais da precipitação pluvial e de mais 11 índices climáticos pluviais definidos pela OMM (Organização Meteorológica Mundial) obtidas a partir de dados diários de 258 estações meteorológicas e/ou postos pluviométricos, fornecidos pela antiga rede de postos da SUDENE/DCA, referentes a um período de 47 anos (1960-2006). Com base nesses dados, foram aplicadas as técnicas de ACP e AA à média pluvial e aos 11 índices pluviais. Na ACP, nove índices climáticos e a média pluvial foram representados por três componentes principais e estas explicaram mais de 90% da variância original dos dados. Na AA, nove índices apresentaram quatro grupos homogêneos de atuação.
Palavras - chave: Componentes principais, agrupamento, índices pluviais.
Rain Study in Northeast Brazil Using Multivariate Analysis
ABSTRACT
The aim of this work was to study the dynamics of spatial and temporal climatic variability in rainfall in the nine states of Northeast Brazil, using the multivariate techniques of Principal Component Analysis (PCA) and Cluster Analysis (CA). We used monthly averages of rainfall and 11 climate indices over rain defined by WMO (World Meteorological Organization) obtained from daily data from 258 meteorological stations and/or climatic stations, supplied by the former service station network SUDENE/DCA, referring a period of 47 years (1960-2006). Based on these data, we applied the techniques the average PCA and CA rain and 11 rain indices. In ACP, nine climate indices and average rainfall were represented by three principal components and these accounted for more than 90% of the variance of the original data. In AA, nine indices showed four homogeneous groups of activity.
Keywords: Principal components; cluster; rain indices.
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Copyright (c) 2012 Winicius Santos Araújo, Francisco Assis Saviano Souza, José Ivaldo Barbosa de Brito, Lourivaldo Mota Lima
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