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		<doi>10.1590/S0102-77862012000100007</doi>
		<issn>0102-7786</issn>
		<label>lattes: 2681016875171472 1 PessoaLiSiStSTCAFe:2012:MiDaMe</label>
		<citationkey>PessoaLiSiStStCaFe:2012:MiDaMe</citationkey>
		<title>Mineração de dados meteorológicos para previsão de eventos severos</title>
		<project>CNPq (“Cb-Mining”, processo 479510/2006-7), FINEP (projeto ADAPT)</project>
		<year>2012</year>
		<secondarytype>PRE PN</secondarytype>
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		<author>Pessoa, Alex Sandro Aguiar,</author>
		<author>Lima, Glauston Roberto Teixeira de,</author>
		<author>Silva, José Demísio Simões da,</author>
		<author>Stephany, Stephan,</author>
		<author>Strauss, Cesar,</author>
		<author>Caetano, Mirian,</author>
		<author>Ferreira, Nelson Jesus,</author>
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		<group>CPT-CPT-INPE-MCTI-GOV-BR</group>
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		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>asapessoa@gmail.com</electronicmailaddress>
		<electronicmailaddress>glau11@gmail.com</electronicmailaddress>
		<electronicmailaddress>demisio@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>stephan@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>cstrauss@cea.inpe.br</electronicmailaddress>
		<electronicmailaddress>mirian.caetano@cptec.inpe.br</electronicmailaddress>
		<electronicmailaddress>nelson.ferreira@cptec.inpe.br</electronicmailaddress>
		<e-mailaddress>asapessoa@gmail.com</e-mailaddress>
		<journal>Revista Brasileira de Meteorologia</journal>
		<volume>27</volume>
		<number>1</number>
		<pages>61-74</pages>
		<transferableflag>1</transferableflag>
		<contenttype>External Contribution</contenttype>
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		<keywords>mineração de dados, previsão meteorológica, eventos convectivos, data mining, weather forecast, convective events.</keywords>
		<abstract>O objetivo do trabalho proposto é detectar antecipadamente possíveis ocorrências de eventos convectivos severos, por meio do monitoramento das saídas do modelo de previsão numérica de tempo Eta, para cada intervalo de previsão e para um conjunto de variáveis selecionadas. O período de estudo estende-se de janeiro a fevereiro de 2007. Classificadores foram desenvolvidos pela abordagem de similaridade de vetores e de conjuntos aproximativos, de forma a identificar saídas do modelo Eta que possam ser associados a esses eventos. Assumiu-se como premissa que os eventos convectivos severos possam ser correlacionados com grande número de ocorrências de descargas elétricas atmosféricas. Os classificadores agruparam as saídas do modelo Eta, compostas por essas variáveis, com base na densidade de ocorrência de descargas elétricas atmosféricas nuvem-solo. Ambos os classificadores apresentaram bom desempenho para os testes realizados para um período de dois meses escolhido para três mini-regiões selecionadas do território brasileiro. ABSTRACT: This work aims the early detection of possible occurrences of severe convective events in Central and Southeast Brazil by means of monitoring the output of the Eta numerical weather prediction model for each forecasted time interval and for a selected set of variables. The studied period ranges from January to February 2007. Classifiers were developed by two approaches, vector similarity and rough sets, in order to identify Eta outputs that can be associated to such events. It was assumed that severe convective events can be correlated to a large number of atmospheric electric discharges. The classifiers grouped the Eta meteorological model outputs for these selected variables based on the density of occurrences of cloud-to-ground atmospheric electrical discharges. Both classifiers show good performance for the chosen 2-month period at the three selected mini-regions of the Brazilian territory.</abstract>
		<area>COMP</area>
		<language>pt</language>
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		<url>http://www.rbmet.org.br/port/revista/revista_artigo.php?id_artigo=1087</url>
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