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Metadados

1. Identificação
Tipo de ReferênciaArtigo em Evento (Conference Proceedings)
Sitemtc-m16b.sid.inpe.br
Código do Detentorisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Repositóriocptec.inpe.br/adm_conf/2005/10.31.12.09
Última Atualização2006:04.16.17.11.52 (UTC) administrator
Repositório de Metadadoscptec.inpe.br/adm_conf/2005/10.31.12.09.18
Última Atualização dos Metadados2021:02.10.19.21.57 (UTC) administrator
Chave SecundáriaINPE-13848-PRE/9030
Chave de CitaçãoDiasMoreDoli:2006:MaSuMo
TítuloThe Master Super Model Ensemble System (MSMES)
FormatoCD-ROM; On-line.
Ano2006
Data de Acesso26 dez. 2024
Tipo SecundárioPRE CI
Número de Arquivos1
Tamanho312 KiB
2. Contextualização
Autor1 Dias, Pedro Leite da Silva
2 Moreira, Demerval Soares
3 Dolif Neto, Giovanni
Grupo1 DOP-INPE-MCT-BR
2 DOP-INPE-MCT-BR
Afiliação1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Instituto Nacional de Pesquisas Espaciais (INPE)
Endereço de e-Mail do Autor1 pldsdias@master.iag.usp.br
2 demerval@cptec.inpe.br
3 giovanni@cptec.inpe.br
EditorVera, Carolina
Nobre, Carlos
Endereço de e-Maildemerval@cptec.inpe.br
Nome do EventoInternational Conference on Southern Hemisphere Meteorology and Oceanography, 8 (ICSHMO).
Localização do EventoFoz do Iguaçu
Data24-28 Apr. 2006
Editora (Publisher)American Meteorological Society (AMS)
Cidade da Editora45 Beacon Hill Road, Boston, MA, USA
Páginas1751-1757
Título do LivroProceedings
Tipo TerciárioPoster
OrganizaçãoAmerican Meteorological Society (AMS)
Histórico (UTC)2005-10-31 12:09:19 :: demerval@cptec.inpe.br -> administrator ::
2005-11-11 21:53:46 :: administrator -> adm_conf ::
2005-12-15 16:46:41 :: adm_conf -> demerval@cptec.inpe.br ::
2006-03-29 18:44:46 :: demerval@cptec.inpe.br -> administrator ::
2006-04-18 21:05:07 :: administrator -> lise@dpi.inpe.br ::
2010-12-28 12:36:32 :: lise@dpi.inpe.br -> administrator ::
2010-12-29 15:56:58 :: administrator -> lise@dpi.inpe.br :: 2006
2010-12-29 16:05:55 :: lise@dpi.inpe.br -> administrator :: 2006
2010-12-29 18:52:46 :: administrator -> banon :: 2006
2011-01-02 17:14:55 :: banon -> administrator :: 2006
2021-02-10 19:21:57 :: administrator -> :: 2006
3. Conteúdo e estrutura
É a matriz ou uma cópia?é a matriz
Estágio do Conteúdoconcluido
Transferível1
Palavras-ChaveSuper-Model ensemble
statistical model
mean square error
bias
uncertainness index
ResumoA statistical model of weather forecast has been implemented at the weather laboratory at the University of São Paulo (MASTER/IAG/USP Laboratory - www.master.iag.usp.br). This statistical forecast is obtained on a routine daily basis from an ensemble of six global models and fourteen regional models of numerical weather prediction (NWP). The optimal combination of the several individual forecasts is obtained by the weighted mean of the forecasts after bias removal. The weights are provided by the inverse of the mean square error (MSE) of each forecast. The evaluation metric is based on the fit of the forecast to the surface data. METAR, SYNOP and the Center for Weather Forecasting and Climate Studies CPTEC automatic weather stations. . The predicted variables are: a) temperature; b) dew point temperature; c) zonal wind; d) meridional wind; e) sea level pressure; f) precipitation. Precipitation estimates provided by TRMM, NAVY and CPTEC are treated separately. To evaluate the statistical model, the MSE and bias averaged in 15 days period are calculated for each station. The choice of the 15 days period is based on the fact that the forecast errors are somewhat influences by the intraseasonal oscillation. Real time forecasts are available at the MASTER homepage. The statistical models evaluation indicates that the products are very robust and competitive. Separate evaluation is provided for different regions of Brazil and the statistical combination is better than any individual forecast in the mean sense. Concerning precipitation, the results are not statistically sound for the 6 hour accumulated precipitation (TRMM and NAVY), but for 24 hours accumulated precipitation the results are very robust. To appraise the accuracy of this forecast an uncertainness index is calculated. Low values of this index indicate that there isn't large dispersion between forecasts and also indicate that these forecasts are similar to statistical model forecast, thus increasing the confidence in the statistical forecasts.
ÁreaMET
TipoWeather analysis and forecasting
Arranjourlib.net > BDMCI > Fonds > Produção anterior à 2021 > DIDOP > The Master Super...
Conteúdo da Pasta docacessar
Conteúdo da Pasta source
master_superensemble_v3.doc 29/03/2006 15:44 145.0 KiB 
Conteúdo da Pasta agreementnão têm arquivos
4. Condições de acesso e uso
URL dos dadoshttp://urlib.net/ibi/cptec.inpe.br/adm_conf/2005/10.31.12.09
URL dos dados zipadoshttp://urlib.net/zip/cptec.inpe.br/adm_conf/2005/10.31.12.09
Idiomaen
Arquivo Alvo1751-1758.pdf
Grupo de Usuáriosadministrator
demerval@cptec.inpe.br
lise@dpi.inpe.br
administrator
banon
Visibilidadeshown
Detentor da CópiaSID/SCD
Permissão de Leituraallow from all
5. Fontes relacionadas
Unidades Imediatamente Superiores8JMKD3MGPCW/43SQKNE
Lista de Itens Citandosid.inpe.br/bibdigital/2021/01.02.22.14 6
Acervo Hospedeirocptec.inpe.br/nobre/2005/06.02.21.14
cptec.inpe.br/walmeida/2003/04.25.17.12
6. Notas
Nota1
Campos Vaziosarchivingpolicy archivist callnumber contenttype copyright creatorhistory descriptionlevel dissemination documentstage doi edition identifier isbn issn label lineage mirrorrepository nextedition notes numberofvolumes orcid parameterlist parentrepositories previousedition previouslowerunit progress project readergroup resumeid rightsholder schedulinginformation secondarydate secondarymark serieseditor session shorttitle sponsor subject tertiarymark url versiontype volume


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