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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP7W/3CR8C35
Repositóriosid.inpe.br/mtc-m19/2012/10.17.17.02
Última Atualização2012:10.17.17.38.20 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m19/2012/10.17.17.02.43
Última Atualização dos Metadados2018:06.05.04.13.12 (UTC) administrator
Chave SecundáriaINPE--PRE/
ISSN2238-1007
Chave de CitaçãoCintraVelh:2012:GlDaAs
TítuloGlobal Data Assimilation Using Artificial Neural Networks in Speedy Model
Ano2012
Data de Acesso23 jun. 2024
Tipo SecundárioPRE CN
Número de Arquivos1
Tamanho875 KiB
2. Contextualização
Autor1 Cintra, Rosangela. S.
2 Velho, Haroldo F. de Campos
Grupo1
2 LAC-CTE-INPE-MCTI-GOV-BR
Afiliação1
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 rosangela.cintra@lac.inpe.br
2 haroldo@lac.inpe.br
Nome do EventoInternational Symposium on Uncertainty Quantification and Stochastic Modeling, 1.
Localização do EventoSão Sebastião, SP
DataFeb. 26th to Mar. 2nd, 2012
Páginas648-654
Título do LivroProceedings
Histórico (UTC)2012-10-17 17:38:20 :: marcelo.pazos -> administrator :: 2012
2018-06-05 04:13:12 :: administrator -> marcelo.pazos@inpe.br :: 2012
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Tipo do ConteúdoExternal Contribution
Palavras-Chavedata assimilation
artificial neural network
ensemble kalman Filter
numerical weather forecasting
ResumoWeather forecasting systems require a model for the time evolution and an estimate of the current state of the system. Data assimilation provides such an initial estimate of the atmosphere where it combines information from observations and from a prior short-term forecast producing an current state estimate. An Artificial Neural Network (ANN) is designed for data assimilation. The use of observations from the earth-orbiting satellites in operational numerical prediction models is performed for improving weather forecasts. The data related to atmospheric, oceanic, and land surface state from satellites provides increasingly large volumes. However, the use of this amount of data increases the computational effort. The goal here is to simulate the process for assimilating temperature data computed from satellite radiances. The numerical experiment is carried out with the global model Simplified parameterizations, primitive-Equation Dynamics (SPEEDY ) with simplified physical processes of an atmospheric general circulation in tri-dimensional coordinates. For the data assimilation scheme was applied an ANN: a Multilayer Perceptron(MLP) with supervised training. The MLP-ANN is able to emulate the analysis from the Local Ensemble Transform Kalman Filter(LETKF). LETKF is a version of Kalman Filter with Monte-Carlo ensembles of short-term forecasts. In this experiment, the MLP-ANN was trained with supervision from first six months considering the years 1982, 1983, and 1984. A hindcasting experiment for data assimilation performed a cycle for january of 1985 with MLP-NN, LETKF and SPEEDY model. The synthetic temperature observations were used. The numerical results demonstrate the effectiveness of this ANN technique on atmospheric data assimilation. The results for analysis with ANN are very close with the results from LETKF data assimilation. The simulations show that the major advantage of using MLP-NN is the better computational performance, with similar quality of analysis. The CPU-time assimilation with MLP-NN is 75% less than LETKF with the same observations. Actually, considering the supervised ANN for data assimilation, the most relevant issue is the computational speed-up for computing the analyzed initial condition for state model that accelerates the whole process of numerical weather prediction.
ÁreaCOMP
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Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 17/10/2012 14:02 1.0 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP7W/3CR8C35
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP7W/3CR8C35
Arquivo Alvo106RCintra.pdf
Grupo de Usuáriosmarcelo.pazos@inpe.br
Visibilidadeshown
Permissão de Leituraallow from all
Permissão de Atualizaçãonão transferida
5. Fontes relacionadas
Repositório Espelhosid.inpe.br/mtc-m19@80/2009/08.21.17.02.53
Unidades Imediatamente Superiores8JMKD3MGPCW/3ESGTTP
Acervo Hospedeirosid.inpe.br/mtc-m19@80/2009/08.21.17.02
6. Notas
Campos Vaziosarchivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition editor format isbn label language lineage mark nextedition notes numberofvolumes orcid organization parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url versiontype volume
7. Controle da descrição
e-Mail (login)marcelo.pazos@inpe.br
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