@Article{GonçalvesSNBMCHT:2006:EvMoRe,
               author = "De Gon{\c{c}}alves, L. G. and Shuttleworth, W. James and Nijssen, 
                         Bart and Burke, Eleanor J. and Marengo, Jose Antonio and Chan, 
                         Chou Sin and Houser, Paul and Toll, David L.",
          affiliation = "{Nasa Goddard Space Flight Center} and Department of Hydrology and 
                         Water Resources, University of Arizona and Department of Hydrology 
                         and Water Resources, University of Arizona and Hadley Centre for 
                         Climate Prediction and Research, Met Office, Exeter, UK and 
                         {Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and Center for Research on 
                         Environment and Water, George Mason University and Hydrological 
                         Sciences Branch, Code 614.3, NASA Goddard Space Flight Center",
                title = "Evaluation of model-derived and remotely sensed precipitation 
                         products for continental South America",
              journal = "Revista Brasileira de Geof{\'{\i}}sica",
                 year = "2006",
               volume = "111",
               number = "D16113",
                pages = "doi:10.1029/2005JD006276",
                month = "Ago",
                 note = "doi:10.1029/2005JD006276",
             keywords = "precipitation, South America, remotely sensed evaluation.",
             abstract = "This paper investigates the reliability of some of the more 
                         important remotely sensed daily precipitation products available 
                         for South America as a precursor to the possible implementation of 
                         a South America Land Data Assimilation System. Precipitation data 
                         fields calculated as 6 hour predictions by the CPTEC Eta model and 
                         three different satellite-derived estimates of precipitation 
                         (Precipitation Estimation from Remotely Sensed Information using 
                         Artificial Neural Networks (PERSIANN), National Environmental 
                         Satellite, Data and Information Service (NESDIS), and Tropical 
                         Rainfall Measuring Mission (TRMM)) are compared with the available 
                         observations of daily total rainfall across South America. To make 
                         this comparison, the threat score, fractional-covered area, and 
                         relative volumetric bias of the model-calculated and remotely 
                         sensed estimates are computed for the year 2000. The results show 
                         that the Eta model-calculated data and the NESDIS product capture 
                         the area without precipitation within the domain reasonably well, 
                         while the TRMM and PERSIANN products tend to underestimate the 
                         area without precipitation and to heavily overestimate the area 
                         with a small amount of precipitation. In terms of precipitation 
                         amount the NESDIS product significantly overestimates and the TRMM 
                         product significantly underestimates precipitation, while the Eta 
                         model-calculated data and PERSIANN product broadly match the 
                         domain average observations. However, both tend to bias the zonal 
                         location of precipitation more heavily toward the equator than the 
                         observations. In general, the Eta model-calculated data outperform 
                         the several remotely sensed data products currently available and 
                         evaluated in the present study.",
           copyholder = "SID/SCD",
                 issn = "0102-261X",
             language = "en",
           targetfile = "Goncalves_Evaluation.pdf",
        urlaccessdate = "30 jun. 2024"
}