@Article{AmaralBVPVPBSPMGD:2018:AsGrDa,
               author = "Amaral, Lia Martins Costa do and Barbieri, Stefano and Vila, 
                         Daniel Alejandro and Puca, Silvia and Vulpiani, Gianfranco and 
                         Panegrossi, Giulia and Biscaro, Thiago Souza and San{\`o}, Paolo 
                         and Petracca, Marco and Marra, Anna Cinzia and Gosset, Marielle 
                         and Dietrich, Stefano",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University 
                         of L’Aquila} and {Instituto Nacional de Pesquisas Espaciais 
                         (INPE)} and {Italian Civil Protection Department} and {Italian 
                         Civil Protection Department} and {Institute of Atmospheric 
                         Sciences and Climate (ISAC) National Research Council of Italy 
                         (CNR)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         {Institute of Atmospheric Sciences and Climate (ISAC) National 
                         Research Council of Italy (CNR)} and {Italian Civil Protection 
                         Department} and {Institute of Atmospheric Sciences and Climate 
                         (ISAC) National Research Council of Italy (CNR)} and {Institute of 
                         Research for Development (IRD)} and {Institute of Atmospheric 
                         Sciences and Climate (ISAC) National Research Council of Italy 
                         (CNR)}",
                title = "Assessment of ground-reference data and validation of the H-SAF 
                         precipitation products in Brazil",
              journal = "Remote Sensing",
                 year = "2018",
               volume = "10",
               number = "11",
                pages = "e1743",
                month = "Nov.",
             keywords = "rain gauges, radar, quality indexes, satellite rainfall 
                         retrievals, validation.",
             abstract = "The uncertainties associated with rainfall estimates comprise 
                         various measurement scales: from rain gauges and ground-based 
                         radars to the satellite rainfall retrievals. The quality of 
                         satellite rainfall products has improved significantly in recent 
                         decades; however, such algorithms require validation studies using 
                         observational rainfall data. For this reason, this study aims to 
                         apply the H-SAF consolidated radar data processing to the X-band 
                         radar used in the CHUVA campaigns and apply the well established 
                         H-SAF validation procedure to these data and verify the quality of 
                         EUMETSAT H-SAF operational passive microwave precipitation 
                         products in two regions of Brazil (Vale do Para{\'{\i}}ba and 
                         Manaus). These products are based on two rainfall retrieval 
                         algorithms: the physically based Bayesian Cloud Dynamics and 
                         Radiation Database (CDRD algorithm) for SSMI/S sensors and the 
                         Passive microwave Neural network Precipitation Retrieval algorithm 
                         (PNPR) for cross-track scanning radiometers (AMSU-A/AMSU-B/MHS 
                         sensors) and for the ATMS sensor. These algorithms, optimized for 
                         Europe, Africa and the Southern Atlantic region, provide estimates 
                         for the MSG full disk area. Firstly, the radar data was treated 
                         with an overall quality index which includes corrections for 
                         different error sources like ground clutter, range distance, 
                         rain-induced attenuation, among others. Different polarimetric and 
                         non-polarimetric QPE algorithms have been tested and the Vulpiani 
                         algorithm (hereafter, Rq2Vu15) presents the best precipitation 
                         retrievals when compared with independent rain gauges. Regarding 
                         the results from satellite-based algorithms, generally, all 
                         rainfall retrievals tend to detect a larger precipitation area 
                         than the ground-based radar and overestimate intense rain rates 
                         for the Manaus region. Such behavior is related to the fact that 
                         the environmental and meteorological conditions of the Amazon 
                         region are not well represented in the algorithms. Differently, 
                         for the Vale do Para{\'{\i}}ba region, the precipitation 
                         patterns were well detected and the estimates are in accordance 
                         with the reference as indicated by the low mean bias values.",
                  doi = "10.3390/rs10111743",
                  url = "http://dx.doi.org/10.3390/rs10111743",
                 issn = "2072-4292",
             language = "en",
           targetfile = "amaral-assessment.pdf",
        urlaccessdate = "27 abr. 2024"
}