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Metadados

1. Identificação
Tipo de ReferênciaResumo em Evento (Conference Proceedings)
Sitemtc-m16d.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identificador8JMKD3MGP7W/3D9SGSP
Repositóriosid.inpe.br/mtc-m19/2012/12.28.12.17
Última Atualização2015:03.18.18.52.02 (UTC) administrator
Repositório de Metadadossid.inpe.br/mtc-m19/2012/12.28.12.17.02
Última Atualização dos Metadados2018:06.05.04.13.34 (UTC) administrator
Chave SecundáriaINPE--PRE/
Chave de CitaçãoCintraCamp:2012:SaOb
TítuloGlobal Temperature Assimilation using Artificial Neural Networks in SPEEDY Model: Satellite Observation
Ano2012
Data de Acesso07 maio 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho38 KiB
2. Contextualização
Autor1 Cintra, Rosangela Saher Correa
2 Campos Velho, H. F.
Identificador de Curriculo1 8JMKD3MGP5W/3C9JJ75
Grupo1 LAC-CTE-INPE-MCTI-GOV-BR
2 LAC-CTE-INPE-MCTI-GOV-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 rosangela.cintra@lac.inpe.br
2 haroldo@lac.inpe.br
Endereço de e-Mailmarcelo.pazos@inpe.br
Nome do EventoEuropean Geosciences Union (EGU) General Assembly.
Localização do EventoViena
Data22 a 27 de abril de 2012
Título do LivroAbstracts
Histórico (UTC)2012-12-28 12:18:01 :: marcelo.pazos@sid.inpe.br -> administrator :: 2012
2018-06-05 04:13:34 :: 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
Tipo de Versãopublisher
ResumoAn Artificial Neural Network (ANN) is designed to investigate a application for data assimilation. This procedure provides an appropriated initial condition to the atmosphere to numerical weather prediction (NWP). The NWP incorporates the equations of atmospheric dynamics with physical process and it can predict the future state of the atmosphere. Data assimilation procedure combines information from observations and from a prior short-term forecast producing an current state estimate. Operational satellite data are taken and processed in real-time and distributed around the world. The use of observations from the earth-orbiting satellites in operational NWP provides large data volumes and increases the computational effort. The goal here is to simulate the process for assimilating temperature data computed from satellite radiances and introduce new technique in analysis to Weather Forecasting and climate. This performance can be faster than conventional schemes for data assimilation. The numerical experiment is carried out with global model: the Simplified Parameterizations, primitivE-Equation DYnamics (SPEEDY) and the synthetic observations of temperatures from model plus a random noise. For the data assimilation technique was applied a Multilayer Perceptron (MLP-NN) with supervised training, which observation, local point observation and the Local Ensemble Transform Kalman Filter (LETKF) analysis are used as input vector. The global analysis is done in the activation MLP-NN with only, synthetic observation and its local point. In this experiment, the MLP-ANN was trained with the first six months considering the years 1982, 1983, and 1984 data. A hindcasting experiment for data assimilation performed a cycle for January of 1985 with MLP-NN and SPEEDY model. LETKF was performed at the same cycle. The results for MLP-NN analysis are very close with the results obtained from LETKF. The simulations show that the major advantage of using ANN is the better computational performance, with similar quality of analysis. The CPU-time assimilation with MLP-NN is 80% less than LETKF with the same observations.
ÁreaCOMP
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Global Temperature Assimilation...
Conteúdo da Pasta docacessar
Conteúdo da Pasta sourcenão têm arquivos
Conteúdo da Pasta agreement
agreement.html 28/12/2012 10:17 1.0 KiB 
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/8JMKD3MGP7W/3D9SGSP
URL dos dados zipadoshttp://urlib.net/zip/8JMKD3MGP7W/3D9SGSP
Idiomaen
Arquivo Alvocintra_global.pdf
Grupo de Usuáriosmarcelo.pazos@inpe.br
Grupo de Leitoresadministrator
marcelo.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 edition editor format isbn issn keywords label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark tertiarytype type url volume
7. Controle da descrição
e-Mail (login)marcelo.pazos@inpe.br
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