@Article{OliveiraMaggVilaMora:2016:ChDiCy,
               author = "Oliveira, R{\^o}mulo Augusto Juc{\'a} and Maggioni, Viviana and 
                         Vila, Daniel Alejandro and Morales, Carlos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {George 
                         Mason University (GMU)} and {Instituto Nacional de Pesquisas 
                         Espaciais (INPE)} and {Universidade de S{\~a}o Paulo (USP)}",
                title = "Characteristics and diurnal cycle of GPM rainfall estimates over 
                         the central Amazon region",
              journal = "Remote Sensing",
                 year = "2016",
               volume = "8",
               number = "7",
                month = "July",
             keywords = "satellite rainfall estimates, radar rainfall estimates, GPM, 
                         IMERG, GPROF, uncertainty quantification, GoAmazon.",
             abstract = "Studies that investigate and evaluate the quality, limitations and 
                         uncertainties of satellite rainfall estimates are fundamental to 
                         assure the correct and successful use of these products in 
                         applications, such as climate studies, hydrological modeling and 
                         natural hazard monitoring. Over regions of the globe that lack in 
                         situ observations, such studies are only possible through 
                         intensive field measurement campaigns, which provide a range of 
                         high quality ground measurements, e.g., CHUVA (Cloud processes of 
                         tHe main precipitation systems in Brazil: A contribUtion to cloud 
                         resolVing modeling and to the GlobAl Precipitation Measurement) 
                         and GoAmazon (Observations and Modeling of the Green Ocean Amazon) 
                         over the Brazilian Amazon during 2014/2015. This study aims to 
                         assess the characteristics of Global Precipitation Measurement 
                         (GPM) satellite-based precipitation estimates in representing the 
                         diurnal cycle over the Brazilian Amazon. The Integrated 
                         Multi-satellitE Retrievals for Global Precipitation Measurement 
                         (IMERG) and the Goddard Profiling Algorithm-Version 2014 
                         (GPROF2014) algorithms are evaluated against ground-based radar 
                         observations. Specifically, the S-band weather radar from the 
                         Amazon Protection National System (SIPAM), is first validated 
                         against the X-band CHUVA radar and then used as a reference to 
                         evaluate GPM precipitation. Results showed satisfactory agreement 
                         between S-band SIPAM radar and both IMERG and GPROF2014 
                         algorithms. However, during the wet season, IMERG, which uses the 
                         GPROF2014 rainfall retrieval from the GPM Microwave Imager (GMI) 
                         sensor, significantly overestimates the frequency of heavy 
                         rainfall volumes around 00:00-04:00 UTC and 15:00-18:00 UTC. This 
                         overestimation is particularly evident over the Negro, Solimoes 
                         and Amazon rivers due to the poorly-calibrated algorithm over 
                         water surfaces. On the other hand, during the dry season, the 
                         IMERG product underestimates mean precipitation in comparison to 
                         the S-band SIPAM radar, mainly due to the fact that isolated 
                         convective rain cells in the afternoon are not detected by the 
                         satellite precipitation algorithm.",
                  doi = "10.3390/rs8070544",
                  url = "http://dx.doi.org/10.3390/rs8070544",
                 issn = "2072-4292",
             language = "en",
           targetfile = "Oliveira_characteristics.pdf",
        urlaccessdate = "28 abr. 2024"
}