1. Identificação | |
Tipo de Referência | Artigo em Revista Científica (Journal Article) |
Site | mtc-m21d.sid.inpe.br |
Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S |
Identificador | 8JMKD3MGP3W34T/46HPDAP |
Repositório | sid.inpe.br/mtc-m21d/2022/03.21.12.17 (acesso restrito) |
Última Atualização | 2022:03.21.12.17.52 (UTC) simone |
Repositório de Metadados | sid.inpe.br/mtc-m21d/2022/03.21.12.17.52 |
Última Atualização dos Metadados | 2023:01.03.16.46.03 (UTC) administrator |
DOI | 10.1186/s40537-022-00580-9 |
ISSN | 2196-1115 |
Chave de Citação | RamosTaCuSiGoDi:2022:CaMoSe |
Título | A canonical model for seasonal climate prediction using Big Data |
Ano | 2022 |
Mês | Dec. |
Data de Acesso | 18 maio 2024 |
Tipo de Trabalho | journal article |
Tipo Secundário | PRE PI |
Número de Arquivos | 1 |
Tamanho | 2674 KiB |
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2. Contextualização | |
Autor | 1 Ramos, Marcelo Paiva 2 Tasinaffo, P. M. 3 Cunha, A. M. 4 Silva, D. A. 5 Gonçalves, G. S. 6 Dias, L. A. V. |
ORCID | 1 0000-0002-8929-0491 |
Grupo | 1 DIPTC-CGCT-INPE-MCTI-GOV-BR |
Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Tecnológico de Aeronáutica (ITA) 3 Instituto Tecnológico de Aeronáutica (ITA) 4 Instituto Tecnológico de Aeronáutica (ITA) 5 Instituto Tecnológico de Aeronáutica (ITA) 6 Instituto Tecnológico de Aeronáutica (ITA) |
Endereço de e-Mail do Autor | 1 marcelopaivaramos@gmail |
Revista | Journal of Big Data |
Volume | 9 |
Número | 1 |
Páginas | e27 |
Histórico (UTC) | 2022-03-21 12:19:05 :: simone -> administrator :: 2022 2023-01-03 16:46:03 :: administrator -> simone :: 2022 |
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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 | Atmospheric numerical model Big Data Hadoop Hive MapReduce Seasonal climate prediction |
Resumo | This article addresses the elaboration of a canonical model, involving methods, techniques, metrics, tools, and Big Data, applied to the knowledge of seasonal climate prediction, aiming at greater dynamics, speed, conciseness, and scalability. The proposed model was hosted in an environment capable of integrating different types of meteorological data and centralizing data stores. The seasonal climate prediction method called M-PRECLIS was designed and developed for practical application. The usability and efficiency of the proposed model was tested through a case study that made use of operational data generated by an atmospheric numerical model of the climate area found in the supercomputing environment of the Center for Weather Forecasting and Climate Studies linked to the Brazilian Institute for Space Research. The seasonal climate prediction uses ensemble members method to work and the main Big Data technologies used for data processing were: Python language, Apache Hadoop, Apache Hive, and the Optimized Row Columnar (ORC) file format. The main contributions of this research are the canonical model, its modules and internal components, the proposed method M-PRECLIS, and its use in a case study. After applying the model to a practical and real experiment, it was possible to analyze the results obtained and verify: the consistency of the model by the output images, the code complexity, the performance, and also to perform the comparison with related works. Thus, it was found that the proposed canonical model, based on the best practices of Big Data, is a viable alternative that can guide new paths to be followed. |
Área | MET |
Arranjo | A canonical model... |
Conteúdo da Pasta doc | acessar |
Conteúdo da Pasta source | não têm arquivos |
Conteúdo da Pasta agreement | |
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4. Condições de acesso e uso | |
Idioma | en |
Arquivo Alvo | ramos_2022_canonical.pdf |
Grupo de Usuários | simone |
Grupo de Leitores | administrator simone |
Visibilidade | shown |
Permissão de Leitura | deny from all and allow from 150.163 |
Permissão de Atualização | não transferida |
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5. Fontes relacionadas | |
Unidades Imediatamente Superiores | 8JMKD3MGPCW/46KUATE |
Lista de Itens Citando | sid.inpe.br/bibdigital/2022/04.03.22.23 1 |
Divulgação | WEBSCI; PORTALCAPES; SCOPUS. |
Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 |
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6. Notas | |
Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url |
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7. Controle da descrição | |
e-Mail (login) | simone |
atualizar | |
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