| 1. Identificação | |
| Tipo de Referência | Capítulo de Livro (Book Section) |
| Site | plutao.sid.inpe.br |
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
| Identificador | 8JMKD3MGP3W/3RAQQLE |
| Repositório | sid.inpe.br/plutao/2018/06.19.04.14 (acesso restrito) |
| Última Atualização | 2018:06.20.13.43.25 (UTC) simone |
| Repositório de Metadados | sid.inpe.br/plutao/2018/06.19.04.14.06 |
| Última Atualização dos Metadados | 2024:01.03.12.42.18 (UTC) simone |
| ISBN | 9789535137801 |
| Rótulo | lattes: 5142426481528206 2 CintraCamp:2018:DaAsAr |
| Chave de Citação | CintraCamp:2018:DaAsAr |
| Título | Data assimilation by artificial neural networks for an atmospheric general circulation model  |
| Ano | 2018 |
| Data de Acesso | 12 mar. 2025 |
| Tipo Secundário | PRE LI |
| Número de Arquivos | 1 |
| Tamanho | 4817 KiB |
| 2. Contextualização | |
| Autor | 1 Cintra, Rosangela Saher Corrêa 2 Campos Velho, Haroldo Fraga de |
| Identificador de Curriculo | 1 8JMKD3MGP5W/3C9JJ75 2 8JMKD3MGP5W/3C9JHC3 |
| Grupo | 1 2 LABAC-COCTE-INPE-MCTIC-GOV-BR |
| Afiliação | 1 2 Instituto Nacional de Pesquisas Espaciais (INPE) |
| Endereço de e-Mail do Autor | 1 2 haroldo.camposvelho@inpe.br |
| Editor | El-Shahat, A. |
| Título do Livro | Advanced applications for artificial neural Networks |
| Editora (Publisher) | Intech |
| Cidade | Janeza Trdine (Rijeka) Croatia |
| Páginas | 265-285 |
| Histórico (UTC) | 2018-06-19 04:14:06 :: lattes -> administrator :: 2018-06-19 11:34:30 :: administrator -> lattes :: 2018 2018-06-20 13:43:26 :: lattes -> administrator :: 2018 2019-01-14 17:09:18 :: administrator -> simone :: 2018 |
| 3. Conteúdo e estrutura | |
| É a matriz ou uma cópia? | é a matriz |
| Estágio do Conteúdo | concluido |
| Transferível | 1 |
| Tipo do Conteúdo | External Contribution |
| Tipo de Versão | publisher |
| Palavras-Chave | Artificial neural networks Data assilimation Numerical weather prediction Computer performance Ensemble Kalman filter |
| Resumo | Numerical weather prediction (NWP) uses atmospheric general circulation models (AGCMs) to predict weather based on current weather conditions. The process of entering observation data into mathematical model to generate the accurate initial conditions is called data assimilation (DA). It combines observations, forecasting, and filtering step. This paper presents an approach for employing artificial neural networks (NNs) to emulate the local ensemble transform Kalman filter (LETKF) as a method of data assimilation. This assimilation experiment tests the Simplified Parameterizations PrimitivE-Equation Dynamics (SPEEDY) model, an atmospheric general circulation model (AGCM), using synthetic observational data simulating localizations of meteorological balloons. For the data assimilation scheme, the supervised NN, the multilayer perceptrons (MLPs) networks are applied. After the training process, the method, forehead-calling MLP-DA, is seen as a function of data assimilation. The NNs were trained with data from first 3 months of 1982, 1983, and 1984. The experiment is performed for January 1985, one data assimilation cycle using MLP-DA with synthetic observations. The numerical results demonstrate the effectiveness of the NN technique for atmospheric data assimilation. The results of the NN analyses are very close to the results from the LETKF analyses, the differences of the monthly average of absolute temperature analyses are of order 102. The simulations show that the major advantage of using the MLP-DA is better computational performance, since the analyses have similar quality. The CPU-time cycle assimilation with MLP-DA analyses is 90 times faster than LETKF cycle assimilation with the mean analyses used to run the forecast experiment. |
| Área | COMP |
| Arranjo | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > LABAC > Data assimilation by... |
| Conteúdo da Pasta doc | acessar |
| Conteúdo da Pasta source | não têm arquivos |
| Conteúdo da Pasta agreement | não têm arquivos |
| 4. Condições de acesso e uso | |
| Idioma | en |
| Arquivo Alvo | cintra_data.pdf |
| Grupo de Usuários | lattes |
| Grupo de Leitores | administrator simone |
| Visibilidade | shown |
| Permissão de Leitura | deny from all and allow from 150.163 |
| Permissão de Atualização | não transferida |
| 5. Fontes relacionadas | |
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3ESGTTP |
| Lista de Itens Citando | sid.inpe.br/bibdigital/2013/09.22.23.14 11 sid.inpe.br/mtc-m21/2012/07.13.14.49.40 7 sid.inpe.br/mtc-m21/2012/07.13.14.59.36 7 |
| URL (dados não confiáveis) | https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/data-assimilation-by-artificial-neural-networks-for-an-atmospheric-general-circulation-model |
| Acervo Hospedeiro | dpi.inpe.br/plutao@80/2008/08.19.15.01 |
| 6. Notas | |
| Campos Vazios | archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel dissemination doi e-mailaddress edition format issn lineage mark mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark serieseditor seriestitle session shorttitle sponsor subject tertiarymark tertiarytype translator volume |
| 7. Controle da descrição | |
| e-Mail (login) | simone |
| atualizar | |