@Article{PalhariniViRoPaMaUn:2022:AnExRa,
               author = "Palharini, Rayana and Vila, Daniel Alejandro and Rodrigues, 
                         Daniele and Palharini, Rodrigo and Mattos, Enrique and Undurraga, 
                         Eduardo",
          affiliation = "{Research Center for Integrated Disaster Risk Management 
                         (CIGIDEN)} and {Instituto Nacional de Pesquisas Espaciais (INPE)} 
                         and {Universidade Federal de Piau{\'{\i}} (UFPI)} and {Federico 
                         Santa Mar{\'{\i}}a Technical University} and {Universidade 
                         Federal de Itajub{\'a} (UNIFEI)} and {Research Center for 
                         Integrated Disaster Risk Management (CIGIDEN)}",
                title = "Analysis of Extreme Rainfall and Natural Disasters Events Using 
                         Satellite Precipitation Products in Different Regions of Brazil",
              journal = "Atmosphere",
                 year = "2022",
               volume = "13",
               number = "10",
                pages = "e1680",
                month = "Oct.",
             keywords = "extreme events, natural disasters, precipitation, satellite.",
             abstract = "The number of natural disasters triggered by extreme events is 
                         increasing worldwide and significantly impacts modern society. 
                         Extreme rainfall is one of the most important factors contributing 
                         to these events. A better understanding of the physical process 
                         that causes extreme rainfall can allow rapid responses from 
                         decision-makers to lessen the impact of natural disasters on the 
                         local population. Satellite monitoring is widely used for this 
                         purpose and is essential for regions where terrestrial 
                         observations are limited or non-existent. The primary purpose of 
                         this study is to describe the performance of satellite products 
                         for extreme rainfall events that caused natural disasters in 
                         various climate regimes in Brazil and discuss the contribution of 
                         mesoscale convective systems (MCS) to these events. We defined 
                         regions based on the climatological rainfall distribution. Cases 
                         with rain values above the 99th percentile during 20122016 were 
                         considered statistically extreme. Our analysis is based on three 
                         datasets, with precipitation from (i) rain gauge stations, (ii) 
                         different satellite-based estimates, and (iii) mesoscale 
                         convective tracking data. The methodology was based on identifying 
                         extreme rainfall events, analyzing the performance of satellite 
                         precipitation estimates and, finally, quantifying the influence of 
                         convective systems on extreme rain. Although all regions of Brazil 
                         may be affected by natural disasters caused by extreme rains, the 
                         results suggest that the impacts caused in each region are 
                         different in magnitude. Convective systems explained over 90% of 
                         extreme rains in the case analyzed in Brazils south and about 60% 
                         to 90% of extreme rains in the case analyzed in the Northeast. In 
                         general, satellite products have identified rain events; however, 
                         in the southern region of Brazil, products have tended to 
                         overestimate rainfall, while other regions have tended to 
                         underestimate extreme rain values. The methods used in satellite 
                         precipitation estimation products have limitations to accurately 
                         identifying specific extreme rain events.",
                  doi = "10.3390/atmos13101680",
                  url = "http://dx.doi.org/10.3390/atmos13101680",
                 issn = "2073-4433",
             language = "en",
           targetfile = "atmosphere-13-01680-v2.pdf",
        urlaccessdate = "06 maio 2024"
}