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		<secondarykey>INPE-10617-PRE/6079</secondarykey>
		<citationkey>NowosadCampRios:2000:NeNeNe</citationkey>
		<title>Neural network as a new approach for data assimilation</title>
		<format>CD-ROM</format>
		<year>2000</year>
		<date>16-20 out. 2000</date>
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		<size>162 KiB</size>
		<author>Nowosad, A. G.,</author>
		<author>Campos Velho, H. F. de,</author>
		<author>Rios Neto, A.,</author>
		<group>DMD-INPE-MCT-BR</group>
		<group>LAC-INPE-MCT-BR</group>
		<affiliation>INPE-Sao Jose dos Campos-12227-010-SP-Brasil</affiliation>
		<e-mailaddress>fabia@cptec.inpe.br</e-mailaddress>
		<conferencename>Congresso Brasileiro de Meteorologia, 11.</conferencename>
		<conferencelocation>Rio de Janeiro</conferencelocation>
		<pages>3078-3086</pages>
		<booktitle>Anais</booktitle>
		<secondarytype>PRE CN</secondarytype>
		<tertiarytype>Artigos</tertiarytype>
		<organization>SBMET</organization>
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		<contenttype>External Contribution</contenttype>
		<keywords>data assimilation, neural networks, Kalman filter, nonlinear dynamics.</keywords>
		<abstract>Multilayer Perceptron Neural Networks are tested as a new method for data assimilation in DYNAMO meteorological model. The approach "emulates" the Kalman Filter data assimilation method avoiding recalculation of the gain matrix at each instant of assimilation. A new prodedure for training the networks is also presented, based on a modification in the backpropagation algorithm. An Adaptive Extended Kalman Filter was used to provide examples for network training.</abstract>
		<area>MET</area>
		<language>pt</language>
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