| 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/47J5NA8 | |||
| Repositório | sid.inpe.br/mtc-m21d/2022/09.05.17.07 (acesso restrito) | |||
| Última Atualização | 2022:09.05.17.07.36 (UTC) simone | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21d/2022/09.05.17.07.36 | |||
| Última Atualização dos Metadados | 2023:01.03.16.46.15 (UTC) administrator | |||
| DOI | 10.1016/j.acags.2022.100099 | |||
| ISSN | 2590-1974 | |||
| Chave de Citação | SilvaFranRuivCamp:2022:WRMaLe | |||
| Título | Forecast of convective events via hybrid model: WRF and machine learning algorithms  | |||
| Ano | 2022 | |||
| Mês | Dec. | |||
| Data de Acesso | 18 maio 2024 | |||
| Tipo de Trabalho | journal article | |||
| Tipo Secundário | PRE PI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 14522 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Silva, Yasmin Uchôa da 2 França, Gutemberg Borges 3 Ruivo, Heloisa Musetti 4 Campos Velho, Haroldo Fraga de | |||
| Identificador de Curriculo | 1 2 3 4 8JMKD3MGP5W/3C9JHC3 | |||
| Grupo | 1 2 3 DIIAV-CGCT-INPE-MCTI-GOV-BR 4 COPDT-CGIP-INPE-MCTI-GOV-BR | |||
| Afiliação | 1 Universidade Federal do Rio de Janeiro (UFRJ) 2 Universidade Federal do Rio de Janeiro (UFRJ) 3 Instituto Nacional de Pesquisas Espaciais (INPE) 4 Instituto Nacional de Pesquisas Espaciais (INPE) | |||
| Endereço de e-Mail do Autor | 1 yasmin@lma.ufrj.br 2 3 helo_mr@hotmail.com 4 haroldo.camposvelho@inpe.br | |||
| Revista | Applied Computing and Geosciences | |||
| Volume | 16 | |||
| Páginas | e100099 | |||
| Histórico (UTC) | 2022-09-05 17:07:56 :: simone -> administrator :: 2022 2023-01-03 16:46:15 :: administrator -> simone :: 2022 | |||
| 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 discharge Convective event Data mining Forecast Machine learning | |||
| Resumo | This presents a novel hybrid 24-h forecasting model of convective weather events based on numerical simulation and machine learning algorithms. To characterize the convective events, 13-year from 2008 up to 2020 of precipitation data from the main airport stations in Rio de Janeiro, Brazil, and atmospheric discharges from the surrounding area of around 150 km are investigated. The Weather Research and Forecasting (WRF) model was used to numerically simulate atmospheric conditions for every day in February, as it is the month with the greatest daily rate of atmospheric discharge for the data period. The p-value hypothesis test (with α=0.05) was applied to each grid point of the numerically predicted variables (defined as an independent attribute) to find those most associated with convective events using the output of the 3-D WRF grid. This one identified 36 attributes (or predictors) that were used as input in the machine learning algorithms' training-test process in this study. Several cross-validation training and testing experiments were carried out using the nine-selected categorical machine learning algorithms and the 36 defined predictors. After applying the boosting technique to the nine previously trained-tested algorithms, the results of the 24-h predictions of convective occurrences were deemed satisfactory. The RandomForest method produced the best results, with statistics values close to perfection, such as POD = 1.00, FAR = 0.02, and CSI = 0.98. The 24-h hindcast utilizing the nine algorithms for the 28 days of February 2019 was very encouraging because it was able to almost recreate the maturation phase of events and their eventual failures were noted during the formation and dissipation phases. The best and worst 24-h hindcast had POD = 0.97 and 0.88, FAR = 0.02 and 0.12, and CSI = 0.94 and 0.78, respectively. | |||
| Área | CST | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > Forecast of convective... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGIP > Forecast of convective... | |||
| 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 | 1-s2.0-S2590197422000210-main.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 | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/46KUATE 8JMKD3MGPCW/46KUES5 | |||
| Lista de Itens Citando | sid.inpe.br/mtc-m21/2012/07.13.14.49.40 4 sid.inpe.br/bibdigital/2022/04.03.22.23 2 sid.inpe.br/bibdigital/2022/04.03.23.11 2 | |||
| Divulgação | PORTALCAPES; SCOPUS. | |||
| Acervo Hospedeiro | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes number orcid parameterlist parentrepositories previousedition previouslowerunit progress project rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||