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@Article{RozanteMorGonVilMor:2010:TeVaOv,
               author = "Rozante, Jos{\'e}Roberto and Moreira, Demerval Soares and de 
                         Goncalves, Luis Gustavo G. and Vila, Daniel A. and Moreira, 
                         Demerval Soares",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)} and {} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Combining TRMM and Surface Observations of Precipitation: 
                         Technique and Validation Over South America",
              journal = "Weather and Forecasting",
                 year = "2010",
               volume = "25",
               number = "3",
                pages = "885--894",
             keywords = "Precipitation, TRMM, Validation.",
             abstract = "The measure of atmospheric model performance is highly dependent 
                         on the quality of the observations used in the evaluation process. 
                         In the particular case of operational forecast centers, 
                         large-scale datasets must be made available in a timely manner for 
                         continuous assessment of model results. Numerical models and 
                         surface observations usually work at distinct spatial scales 
                         (i.e., areal average in a regular grid versus point measurements), 
                         making direct comparison difficult. Alternatively, interpolation 
                         methods are employed for mapping observational data to regular 
                         grids and vice versa. A new technique (hereafter called MERGE) to 
                         combine Tropical Rainfall Measuring Mission (TRMM) satellite 
                         precipitation estimates with surface observations over the South 
                         American continent is proposed and its performance is evaluated 
                         for the 2007 summer and winter seasons. Two different approaches 
                         for the evaluation of the performance of this product against 
                         observations were tested: a cross-validation subsampling of the 
                         entire continent and another subsampling of only areas with sparse 
                         observations. Results show that over areas with a high density of 
                         observations, theMERGEtechniques performance is equivalent to that 
                         of simply averaging the stations within the grid boxes. However, 
                         over areas with sparse observations, MERGE shows superior 
                         results.",
                  doi = "10.1175/2010WAF2222325.1",
                  url = "http://dx.doi.org/10.1175/2010WAF2222325.1",
                 issn = "0882-8156",
                label = "lattes: 0648767431075703 5 RozanteMorGonVilMor:2010:TeVaOv",
             language = "en",
           targetfile = "Rozante_combining.pdf",
        urlaccessdate = "24 jan. 2021"
}


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