@Article{DolifNobr:2012:ArSyPa,
               author = "Dolif, Giovanni and Nobre, Carlos Afonso",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Improving extreme precipitation forecasts in Rio de Janeiro, 
                         Brazil: are synoptic patterns efficient for distinguishing 
                         ordinary from heavy rainfall episodes?",
              journal = "Atmospheric Science Letters",
                 year = "2012",
               volume = "*",
                month = "may",
             keywords = "heavy rainfall forecast, Rio de Janeiro, artificial neural 
                         network, adaptive resonance theory.",
             abstract = "This work analysed heavy rainfall events and their predictability 
                         on Rio de Janeiro, Brazil, using rain gauge data from 2000 to 
                         2010, atmospheric model outputs, and an artificial neural network 
                         based on adaptive resonance theory. The latter was applied on top 
                         of atmospheric simulations for 2009 and 2010, and we were able to 
                         predict 55% of the heavy rainfall events using a combination of 
                         relative humidity at 900 hPa and meridional winds at 10 m for a 
                         domain covering central and southern Brazil, which represents a 
                         relative gain of 67% on predictability when compared to the model 
                         predicted rainfall. Copyright © 2012 Royal Meteorological 
                         Society.",
                  doi = "10.1002/asl.385",
                  url = "http://dx.doi.org/10.1002/asl.385",
                 issn = "1530-261X",
             language = "en",
        urlaccessdate = "16 abr. 2024"
}