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		<isbn>978-85-17-00031-7</isbn>
		<citationkey>CintraSilvCamp:2007:EsPeVa</citationkey>
		<title>Estimativa de perfis de vapor d’água com medidas de radio ocultação utilizando redes neurais artificiais</title>
		<format>CD-ROM; On-line.</format>
		<year>2007</year>
		<secondarytype>PRE CN</secondarytype>
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		<author>Cintra, Rosângela Saher,</author>
		<author>Silva, José Demísio Simões,</author>
		<author>Campos Velho, Haroldo Fraga de,</author>
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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>
		<electronicmailaddress>rosangela.cintra@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>demisio@lac.inpe.br</electronicmailaddress>
		<electronicmailaddress>haroldo@lac.inpe.br</electronicmailaddress>
		<editor>Epiphanio, José Carlos Neves,</editor>
		<editor>Galvão, Lênio Soares,</editor>
		<editor>Fonseca, Leila Maria Garcia,</editor>
		<e-mailaddress>rosangela.cintra@lac.inpe.br</e-mailaddress>
		<conferencename>Simpósio Brasileiro de Sensoriamento Remoto, 13 (SBSR).</conferencename>
		<conferencelocation>Florianópolis</conferencelocation>
		<date>21-26 abr. 2007</date>
		<publisher>Instituto Nacional de Pesquisas Espaciais (INPE)</publisher>
		<publisheraddress>São José dos Campos</publisheraddress>
		<pages>2429-2436</pages>
		<booktitle>Anais</booktitle>
		<tertiarytype>Artigo</tertiarytype>
		<organization>Instituto Nacional de Pesquisas Espaciais (INPE)</organization>
		<transferableflag>1</transferableflag>
		<keywords>vapor water profile, GPS satellite, artificial neural network, multilayer percepton, pressão do vapor d`água, satélites GPS, redes neurais artificiais,  percepton de múltiplas camadas.</keywords>
		<abstract>Artificial Neural Network (ANN) is applied to estimate high-resolution humidity profiles in the troposphere. The use of satellites together Global Positioning System data supply data to study the atmosphere and opens perspectives to improve research on climate and the capacity on weather forecast. In this sense, many techniques were developed for retrieving atmospheric profiles (temperature, pressure and water vapor) using GPS radio occultation. A new method based on ANN to retrieve water vapor profiles is presented. In this paper, a fully connected multi-layer network is constructed. By comparing the retrieved profiles with the corresponding ones from the CHAMP-ISCD (Challenging Mini-satellite satellite Payload for Geoscientific Research and Application), it can be concluded the ANN is convenient and an accurate tool to get humidity profiles. This method constructs humidity profiles from satellite data. The retrieved profiles of water vapor pressure profiles presents bias of 0.06 hPa and maximum standard deviation of 0.75 hPa.  These results can be employed to the atmospheric data assimilation to improve the initial condition of the models of Numerical Weather Prediction.</abstract>
		<area>COMP</area>
		<subject>Geoprocessamento: Aplicações e Modelagem Ambiental</subject>
		<session>Geoprocessamento: Aplicações e Modelagem Ambiental</session>
		<type>Geoprocessamento: Aplicações e Modelagem Ambiental</type>
		<language>pt</language>
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