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@InProceedings{BlacuttHerd:2015:PrCoFo,
               author = "Blacutt, Luis Alberto and Herdies, Dirceu Luis",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
                title = "Precipitation comparison of four datasets over complex topography 
                         region",
                 year = "2015",
         organization = "Join Assembly",
             abstract = "An overwhelming number of applications depend on reliable 
                         precipitation estimations. However, over complex terrain in 
                         regions such as the Andes or the southwestern Amazon, the spatial 
                         coverage of rain gauges is scarce. Two reanalysis datasets, a 
                         satellite algorithm and a scheme that combines surface 
                         observations with satellite estimations were selected for studying 
                         rainfall Bolivia. The selected reanalyses were the Modern-Era 
                         Retrospective Analysis for Research and Applications, which has a 
                         horizontal resolution conducive for studying rainfall in 
                         relatively small precipitation systems, and the Climate Forecast 
                         System Reanalysis and Reforecast, which features an improved 
                         horizontal resolution. The third dataset was the seventh version 
                         of the Tropical Rainfall Measurement Mission 3B42 algorithm. The 
                         fourth dataset utilizes a new technique known as the Combined 
                         Scheme, which successfully removes satellite bias. All four of 
                         these datasets were interpolated to a coarser resolution. This 
                         research aimed to describe and compare precipitation in the two 
                         reanalysis datasets, the satellite-algorithm dataset, and the 
                         Combined Scheme with ground observations. Two seasons were 
                         selected for studying the precipitation estimates: the rainy 
                         season (DecemberFebruary) and the dry season (JuneAugust). The 
                         average, bias, standard deviation, correlation coefficient, and 
                         root mean square error were calculated. Moreover, a contingency 
                         table was generated to calculate the accuracy, bias frequency, 
                         POD, FAR, and ETS. All four datasets correctly depicted the 
                         spatial rainfall pattern. However, CFSR and MERRA overestimated 
                         precipitation along the Andes' easternfacing slopes and exhibited 
                         a dry bias over the eastern Amazon; TRMM3B42 and the Combined 
                         Scheme depicted a more realistic rainfall distribution over both 
                         the Amazon and the Andes. When separating the precipitation into 
                         classes, MERRA and CFSR overestimated light to moderate 
                         precipitation (120 mm/day) and underestimated very heavy 
                         precipitation (>50 mm/day). TRMM3B42 and CoSch depicted behaviors 
                         similar to the surface observations; however, CoSch underestimated 
                         the precipitation in very intense systems (>50 mm/day) The 
                         statistical variables indicated that CoSch's correlation 
                         coefficient was highest for every season and basin.",
  conference-location = "Montreal, Canada",
      conference-year = "3-7 may",
             language = "en",
        urlaccessdate = "05 dez. 2020"
}


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