| 1. Identificação | ||||
| Tipo de Referência | Artigo em Revista Científica (Journal Article) | |||
| Site | mtc-m21c.sid.inpe.br | |||
| Código do Detentor | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identificador | 8JMKD3MGP3W34R/43LSL6H | |||
| Repositório | sid.inpe.br/mtc-m21c/2020/11.27.11.31 | |||
| Última Atualização | 2020:11.27.11.31.52 (UTC) administrator | |||
| Repositório de Metadados | sid.inpe.br/mtc-m21c/2020/11.27.11.31.52 | |||
| Última Atualização dos Metadados | 2022:01.04.01.35.39 (UTC) administrator | |||
| DOI | 10.1080/16000870.2020.1835310 | |||
| ISSN | 0280-6495 | |||
| Chave de Citação | AzevedoGonçKalnWesp:2020:PeMoTe | |||
| Título | Dynamically weighted hybrid gain data assimilation: perfect model testing  | |||
| Ano | 2020 | |||
| Mês | Jan. | |||
| Data de Acesso | 04 maio 2024 | |||
| Tipo de Trabalho | journal article | |||
| Tipo Secundário | PRE PI | |||
| Número de Arquivos | 1 | |||
| Tamanho | 2472 KiB | |||
| 2. Contextualização | ||||
| Autor | 1 Azevedo, Helena Barbieri de 2 Gonçalves, Luis Gustavo Gonçalves de 3 Kalnay, Eugenia 4 Wespetal, Matthew | |||
| Grupo | 1 MET-MET-SESPG-INPE-MCTIC-GOV-BR 2 DIDMD-CGCPT-INPE-MCTIC-GOV-BR | |||
| Afiliação | 1 Instituto Nacional de Pesquisas Espaciais (INPE) 2 Instituto Nacional de Pesquisas Espaciais (INPE) 3 University of Maryland 4 University of Maryland | |||
| Endereço de e-Mail do Autor | 1 helenabdeazevedo@gmail.com 2 luis.goncalves@inpe.br | |||
| Revista | Tellus Series A: Dynamic Meteorology and Oceanography | |||
| Volume | 72 | |||
| Número | 1 | |||
| Histórico (UTC) | 2020-11-27 11:32:41 :: simone -> administrator :: 2020 2022-01-04 01:35:39 :: administrator -> simone :: 2020 | |||
| 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 | data assimilation hybrid systems ensemble Kalman filter numerical weather prediction | |||
| Resumo | Hybrid systems have become the state of the art among data assimilation methods. These systems combine the benefits of two other systems that are traditionally used in operational weather forecasting: an ensemble-based system and a variational system. One of the most recently proposed hybrid approaches is called hybrid gain (HG). It obtains the final analysis as a linear combination of two analyses, assuming that the innovations (i.e. the forecast and the set of observations used) between the two data assimilation methods are identical. A perfect model experiment was performed using the HG in the SPEEDY model to show a new methodology to assign different weights to the two analyses, LETKF and 3D-Var in the generation of the final analysis. Our new approach uses, in the assignment of the weights, the ensemble spread, considered to be a measure of uncertainty in the LETKF. Thus, it is possible to use the estimation of the uncertainty of the analysis that the LETKF provides, to determine where the system should give more weight to the LETKF or the 3D-Var analysis. For this purpose, we define a geographically varying weighting factor alpha, which multiplies the 3D-Var analysis, as the normalised spread for each variable at each level. Then, (1-alpha), which decreases with increasing spread, becomes the factor that multiplies the LETKF analysis. The underlying mechanism of the spread-error relationship is explained using a toy model experiment. The results are very encouraging: the original HG and the new weighted HG analyses have similar high quality and are better than both 3D-Var and LETKF. However, the dynamically weighted HG analyses are significantly more balanced than the original HG analyses are, which has probably contributed to the consistently improved performance observed in the weighted HG, which increases with time throughout the 5-day forecasts. | |||
| Área | MET | |||
| Arranjo 1 | urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > MET > Dynamically weighted hybrid... | |||
| Arranjo 2 | urlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDMD > Dynamically weighted hybrid... | |||
| Conteúdo da Pasta doc | acessar | |||
| Conteúdo da Pasta source | não têm arquivos | |||
| Conteúdo da Pasta agreement |
| |||
| 4. Condições de acesso e uso | ||||
| URL dos dados | http://urlib.net/ibi/8JMKD3MGP3W34R/43LSL6H | |||
| URL dos dados zipados | http://urlib.net/zip/8JMKD3MGP3W34R/43LSL6H | |||
| Idioma | en | |||
| Arquivo Alvo | azevedo_dynamically.pdf | |||
| Grupo de Usuários | simone | |||
| Visibilidade | shown | |||
| Política de Arquivamento | allowpublisher allowfinaldraft | |||
| Permissão de Leitura | allow from all | |||
| Permissão de Atualização | não transferida | |||
| 5. Fontes relacionadas | ||||
| Unidades Imediatamente Superiores | 8JMKD3MGPCW/3F35TRS 8JMKD3MGPCW/43SKC35 | |||
| Lista de Itens Citando | sid.inpe.br/bibdigital/2021/01.01.17.20 3 sid.inpe.br/bibdigital/2013/10.15.01.34 1 | |||
| Divulgação | WEBSCI; PORTALCAPES; SCOPUS. | |||
| Acervo Hospedeiro | urlib.net/www/2017/11.22.19.04 | |||
| 6. Notas | ||||
| Campos Vazios | alternatejournal archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Controle da descrição | ||||
| e-Mail (login) | simone | |||
| atualizar | ||||