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		<issn>2179-4847</issn>
		<citationkey>JorgeCostSant:2020:GeCoNe</citationkey>
		<title>Geographical Complex Networks applied to describe meteorological data</title>
		<format>On-line</format>
		<year>2020</year>
		<secondarytype>PRE CN</secondarytype>
		<numberoffiles>1</numberoffiles>
		<size>1332 KiB</size>
		<author>Jorge, Aurelienne Aparecida Souza,</author>
		<author>Costa, Izabelly Carvalho da,</author>
		<author>Santos, Leonardo B. L.,</author>
		<group>SER-SRE-SESPG-INPE-MCTIC-GOV-BR</group>
		<group>SER-SRE-SESPG-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>Centro Nacional de Monitoramento e Alertas de Desastres Naturais (CEMADEN)</affiliation>
		<electronicmailaddress>aurelienne.jorge@inpe.br</electronicmailaddress>
		<electronicmailaddress>izabelly.costa@inpe.br</electronicmailaddress>
		<electronicmailaddress>santoslbl@gmail.com</electronicmailaddress>
		<editor>Carneiro, Tiago Garcia de Senna (UFOP),</editor>
		<editor>Felgueiras, Carlos Alberto (INPE),</editor>
		<e-mailaddress>lubia@dpi.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Geoinformática, 21 (GEOINFO)</conferencename>
		<conferencelocation>On-line</conferencelocation>
		<date>30 nov. a 03 dez. 2020</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<booktitle>Anais</booktitle>
		<tertiarytype>Short Paper</tertiarytype>
		<transferableflag>1</transferableflag>
		<keywords>banon.</keywords>
		<abstract>ComplexNetworkshavebeenwidelyappliedtoclimatedataanalysis, identifying relations and patterns in the atmosphere on a long-term scale. How- ever, a few investigations have made use of Complex Networks to study meteo- rology (dealing with short-term changes in the atmosphere). With this in mind, the purpose of the present work is to make some progress in the spatial anal- ysis of metrics in meteorological networks, specifically in precipitation events. We present some results for a study case comprising the Tamanduate &#769;&#305; basin, in which we could analyze the spatial dependence intrinsic in the network struc- ture.</abstract>
		<area>SRE</area>
		<subject>Geoinformação</subject>
		<session>Geoinformação</session>
		<type>Geoinformação</type>
		<language>en</language>
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		<username>banon</username>
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		<url>http://mtc-m16c.sid.inpe.br/rep-/sid.inpe.br/mtc-m16c/2020/12.15.13.05</url>
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