@InProceedings{DiasMoreDoli:2006:MaSuMo,
               author = "Dias, Pedro Leite da Silva and Moreira, Demerval Soares and Dolif 
                         Neto, Giovanni",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "The Master Super Model Ensemble System (MSMES)",
            booktitle = "Proceedings...",
                 year = "2006",
               editor = "Vera, Carolina and Nobre, Carlos",
                pages = "1751--1757",
         organization = "International Conference on Southern Hemisphere Meteorology and 
                         Oceanography, 8. (ICSHMO).",
            publisher = "American Meteorological Society (AMS)",
              address = "45 Beacon Hill Road, Boston, MA, USA",
             keywords = "Super-Model ensemble, statistical model, mean square error, bias, 
                         uncertainness index.",
             abstract = "A statistical model of weather forecast has been implemented at 
                         the weather laboratory at the University of S{\~a}o 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.",
  conference-location = "Foz do Igua{\c{c}}u",
      conference-year = "24-28 Apr. 2006",
           copyholder = "SID/SCD",
             language = "en",
         organisation = "American Meteorological Society (AMS)",
                  ibi = "cptec.inpe.br/adm_conf/2005/10.31.12.09",
                  url = "http://urlib.net/ibi/cptec.inpe.br/adm_conf/2005/10.31.12.09",
           targetfile = "1751-1758.pdf",
                 type = "Weather analysis and forecasting",
        urlaccessdate = "17 maio 2024"
}