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@Article{ValverdeArauCamp:2014:NeNeFu,
               author = "Valverde, M. C. and Araujo, E. and Campos Velho, Haroldo Fraga 
                         de",
          affiliation = "{Universidade Federal do ABC (UFABC)} and {Intelig{\^e}ncia 
                         Artificial em Tecnologia (IATECH)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Neural network and fuzzy logic statistical downscaling of 
                         atmospheric circulation-type specific weather pattern for rainfall 
                         forecasting",
              journal = "Applied Soft Computing",
                 year = "2014",
               volume = "22",
                pages = "681--694",
             keywords = "Climatology, Clouds, Disaster prevention, Disasters, Fuzzy logic, 
                         Neural networks, Rain, Soft computing, Weather forecasting, Daily 
                         rainfall forecasting, Multi-linear regression, Natural disasters, 
                         Performance comparison, Rainfall forecasting, South atlantic 
                         convergence zones, Statistical downscaling, Time-spatial series, 
                         Statistics.",
             abstract = "The weather natural disaster prevention for quantitative daily 
                         rainfall forecasting derived from the SACZ-ULCV weather pattern is 
                         proposed in this paper by using intertwined statistical 
                         downscaling (SD) and soft computing (SC) approaches. The fuzzy 
                         statistical downscaling (FSD) is first introduced and, then, 
                         employed for dealing with the SACZ-ULCV atmospheric 
                         circulation-type specific weather pattern for supporting daily 
                         precipitation (rainfall) forecasting. This paper also addresses 
                         the performance comparison of the FSD and the neural statistical 
                         downscaling (NSD) approaches when taking into account 12 major 
                         urban centers all over the state of S{\~a}o Paulo, Brazil, for 
                         the summer period. The SACZ-ULCV summer pattern is identified in 
                         meteorological satellite images when the cloudiness of the 
                         Brazilian Northeast upper level cyclonic vortices (ULCV) meets the 
                         South Atlantic convergence zone (SACZ). Increasing the convection 
                         and the cloudiness over the Southeast region of Brazil, the 
                         SACZ-ULCV causes severe rainfalls and thunderstorms with impact on 
                         the population. Finding a manner to anticipate these extreme 
                         rainfall events is of vital importance for minimizing or avoiding 
                         disasters, and saving lives. Daily rainfall forecasting had their 
                         performance improved either by using the proposed FSD or NSD in 
                         comparison to the Multilinear Regression ETA model. Results 
                         demonstrate the FSD and the NSD become feasible alternatives for 
                         achieving a correspondence from meteorological and 
                         thermo-dynamical variables to the daily rainfall variable.",
                  doi = "10.1016/j.asoc.2014.02.025",
                  url = "http://dx.doi.org/10.1016/j.asoc.2014.02.025",
                 issn = "1568-4946",
                label = "scopus 2014-11 ValverdeArauCamp:2014:NeNeFu",
             language = "en",
        urlaccessdate = "28 nov. 2020"
}


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