<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0187-6236</journal-id>
<journal-title><![CDATA[Atmósfera]]></journal-title>
<abbrev-journal-title><![CDATA[Atmósfera]]></abbrev-journal-title>
<issn>0187-6236</issn>
<publisher>
<publisher-name><![CDATA[Universidad Nacional Autónoma de México, Instituto de Ciencias de la Atmósfera y Cambio Climático]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0187-62362023000300122</article-id>
<article-id pub-id-type="doi">10.20937/atm.53155</article-id>
<title-group>
<article-title xml:lang="en"><![CDATA[Using clustering algorithms and GPM data to identify spatial precipitation patterns over southeastern Brazil]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Guerreiro Miranda]]></surname>
<given-names><![CDATA[Bruno]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Galante Negri]]></surname>
<given-names><![CDATA[Rogério]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Albertani Pampuch]]></surname>
<given-names><![CDATA[Luana]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
<xref ref-type="aff" rid="Aaf"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidade Estadual Paulista Institute of Science and Technology ]]></institution>
<addr-line><![CDATA[São Paulo ]]></addr-line>
<country>Brazil</country>
</aff>
<aff id="Af2">
<institution><![CDATA[,Universidade Estadual Paulista National Center for Monitoring and Early Warning of Natural Disasters ]]></institution>
<addr-line><![CDATA[São Paulo ]]></addr-line>
<country>Brazil</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2023</year>
</pub-date>
<volume>37</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0187-62362023000300122&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0187-62362023000300122&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0187-62362023000300122&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="en"><p><![CDATA[ABSTRACT Southeastern Brazil comprises an important geoeconomic and populous region in South America. Consequently, it is essential to analyze and understand the precipitation profiles in this region. Among different data sources and techniques available to perform such study, the use of clustering algorithms and information from the Global Precipitation Measurement (GPM) project emerges as a convenient, yet less exploited alternative. This study employs the K-Means, the Hierarchical Ward, and the Self-Organizing Maps methods to cluster the annual and seasonal precipitation data from GPM project recorded from 2001 to 2019. The adopted methods are compared in terms of quantitative measures and the number of clusters defined through a well-established rule. The results demonstrate that the annual and seasonal periods are organized according to different number of clusters. Moreover, the results allow: identify the presence of a spatially heterogeneous distribution in the study area; to conclude that the K-Means algorithm is a suitable clustering method in the context of this investigation when compared to Ward&#8217;s Hierarchical and Self-Organizing Maps methods in terms of the Calinski-Harabasz and Davies-Bouldin measures; and that the spatial precipitation distribution over Southeastern Brazil is represented by 10 clusters in annual and summer periods, 11 clusters in autumn and spring and 9 clusters in winter period.]]></p></abstract>
<abstract abstract-type="short" xml:lang="es"><p><![CDATA[RESUMEN El Sudeste de Brasil comprende una importante región geoeconómica y poblada de América del Sur. En consecuencia, es fundamental analizar y comprender los perfiles de precipitación en esta región. Entre las diferentes fuentes de datos y técnicas disponibles para realizar estos estudios, el uso de algoritmos de agrupamiento y la información del proyecto Global Precipitation Measurement (GPM) surge como una alternativa conveniente pero poco explotada. Precisamente, este estudio emplea los métodos K-Means, Hierarchical Ward y Self-Organizing Maps para agrupar los datos de precipitación em subregiones homogénea. Fueran utilizados los períodos anual y estacional registrados de 2001 a 2019 del proyecto GPM. Los métodos adoptados fueron comparados con el uso de medidas cuantitativas y el número de conglomerados definidos mediante una regla bien establecida. Los resultados demuestran que los períodos anuales y estacionales están organizados de acuerdo con diferentes números de conglomerados. Además, los resultados permiten: identificar la presencia de una distribución espacialmente heterogénea en el área de estudio; concluir que el algoritmo K-Means es un método de agrupamiento adecuado en el contexto de esta investigación en comparación con los métodos de Hierarchical Ward y Self-Organizing Maps en términos de las medidas Calinski-Harabasz y Davies-Bouldin; y que la precipitación espacial se distribuye sobre el sureste de Brasil está representada por 10 grupos en períodos anuales y de verano, 11 grupos en otoño y primavera y 9 grupos en período de invierno.]]></p></abstract>
<kwd-group>
<kwd lng="en"><![CDATA[clustering algorithms]]></kwd>
<kwd lng="en"><![CDATA[precipitation]]></kwd>
<kwd lng="en"><![CDATA[southeastern Brazil]]></kwd>
<kwd lng="en"><![CDATA[GPM]]></kwd>
</kwd-group>
</article-meta>
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