@ElectronicSource{ValverdeArauCamp::ArNeNe,
             abstract = "Statistical downscaling method (SD) intertwined with computational 
                         intelligent techniques for quantitative daily rainfall forecasting 
                         of the SACZ-ULCV weather patterns is proposed in this paper. The 
                         SD precipitation forecasting is achieved first with the support of 
                         artificial neural network (ANN) and latter with fuzzy logic (FL). 
                         The SACZ-ULCV occurs when the cloudiness of the upper level 
                         cyclonic vortices (ULCV) in the Brazilian Northeast meets the 
                         South Atlantic Convergence Zone (SACZ) enhancing convection and 
                         cloudiness over the Southeastern region of Brazil. This weather 
                         pattern is responsible for severe rainfalls and thunderstorms. 
                         Finding out a manner to anticipate the severe rainfall produced by 
                         SACZULCV is of vital importance for alerting, or avoiding 
                         disasters. The daily surface rainfall of the southeastern, in 12 
                         major urban centers over the state of S{\~a}o Paulo, is the 
                         output while the dynamical meteorological variables from ETA 
                         regional model are the inputs. The ETA regional model simulates 
                         the large scale dynamical and thermodynamical behavior of the 
                         SACZ-ULCV weather pattern. For this reason, meteorological 
                         variables from ETA model are used to generate statistical 
                         downscaling for the periods of occurrence of the SACZ-ULCV in 
                         summer from 2000 to 2003. Afterwards, the statistical models are 
                         extended to the entire summer period including, thus, other 
                         weather patterns, beside of SACZ-ULCV, for comparative analysis. 
                         Quantitative daily rainfall forecasting results to events of 
                         SACZ-ULCV had their performance improved when was considered only 
                         the model training for SACZ-ULCV periods. The results confirm that 
                         the rain forecast can be improved when used as predictors 
                         dynamical variables obtained from similar weather patterns. On the 
                         other hand, the using FL or AAN models were efficient techniques 
                         as auxiliary mechanisms for SD. Further, both techniques 
                         accomplished better performance when compared to the ETA 
                         meteorological model forecasting.",
              address = "S{\~a}o Jos{\'e} dos Campos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
               author = "Valverde, Maria Cle{\'o}fe and Araujo, Ernesto and Campos Velho, 
                         Haroldo",
             keywords = "fuzzy model, artificial neural network, statistical downscaling, 
                         quantitative rainfall forecasting.",
             language = "en",
       lastupdatedate = "2010-07-13",
            publisher = "Instituto and Nacional and de and Pesquisas and Espaciais",
                  ibi = "8JMKD3MGP7W/37RJRNL",
                  url = "http://urlib.net/ibi/8JMKD3MGP7W/37RJRNL",
           targetfile = "v1.pdf",
                title = "artificial neural network and fuzzy logic statistical downscaling 
                         of atmospheric circulation-type specific for rainfall 
                         forecasting",
         typeofmedium = "On-line",
        urlaccessdate = "27 abr. 2024"
}