author = "Oliveira, R{\^o}mulo Augusto Juc{\'a} and Maggioni, Viviana and 
                         Vila, Daniel Alejandro and Porcacchia, Leonardo",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {George 
                         Mason University} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {George Mason University}",
                title = "Using satellite error modeling to improve GPM-Level 3 rainfall 
                         estimates over the central Amazon region",
              journal = "Remote Sensing",
                 year = "2018",
               volume = "10",
               number = "2",
                pages = "e336",
                month = "Feb.",
             keywords = "global precipitation measurement, IMERG, PUSH, error model, 
                         validation, Amazon.",
             abstract = ": This study aims to assess the characteristics and uncertainty of 
                         Integrated Multisatellite Retrievals for Global Precipitation 
                         Measurement (GPM) (IMERG) Level 3 rainfall estimates and to 
                         improve those estimates using an error model over the central 
                         Amazon region. The S-band Amazon Protection National System 
                         (SIPAM) radar is used as reference and the Precipitation 
                         Uncertainties for Satellite Hydrology (PUSH) framework is adopted 
                         to characterize uncertainties associated with the satellite 
                         precipitation product. PUSH is calibrated and validated for the 
                         study region and takes into account factors like seasonality and 
                         surface type (i.e., land and river). Results demonstrated that the 
                         PUSH model is suitable for characterizing errors in the IMERG 
                         algorithm when compared with S-band SIPAM radar estimates. PUSH 
                         could efficiently predict the satellite rainfall error 
                         distribution in terms of spatial and intensity distribution. 
                         However, an underestimation (overestimation) of light satellite 
                         rain rates was observed during the dry (wet) period, mainly over 
                         rivers. Although the estimated error showed a lower standard 
                         deviation than the observed error, the correlation between 
                         satellite and radar rainfall was high and the systematic error was 
                         well captured along the Negro, Solim{\~o}es, and Amazon rivers, 
                         especially during the wet season.",
                  doi = "10.3390/rs10020336",
                  url = "http://dx.doi.org/10.3390/rs10020336",
                 issn = "2072-4292",
             language = "en",
        urlaccessdate = "13 jun. 2021"