@Article{ValverdeRamirezFerrVelh:2006:LiNoSt,
               author = "Valverde Ramirez, Maria Cleofe and Ferreira, Nelson Jesus and 
                         Velho, Haroldo Fraga de Campos",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)} and {Instituto Nacional de 
                         Pesquisas Espaciais (INPE)}",
                title = "Linear and nonlinear statistical dowscaling for rainfall 
                         forecasting over southeasthern Brazil",
              journal = "Weather and Forecasting",
                 year = "2006",
               volume = "21",
               number = "6",
                pages = "969--989",
                month = "Dec.",
             keywords = "METEOROLOGY, Linear downscaling, Nonlinear downscaling, Rainfall, 
                         METEOROLOGIA, Escala linear, Escala n{\~a}o-linear, Chuvas.",
             abstract = "In this work linear and nonlinear downscaling are developed to 
                         establish empirical relationships between the synoptic-scale 
                         circulation and observed rainfall over southeastern Brazil. The 
                         methodology uses outputs from the regional Eta Model; prognostic 
                         equations for local forecasting were developed using an artificial 
                         neural network (ANN) and multiple linear regression (MLR). The 
                         final objective is the application of such prognostic equations to 
                         Eta Model output to generate rainfall forecasts. In the first 
                         experiment the predictors were obtained from the Eta Model and the 
                         predictand was rainfall data from meteorological stations in 
                         southeastern Brazil. In the second experiment the observed 
                         rainfall on the day prior to the forecast was included as a 
                         predictor. The threat score (TS) and bias, used to quantify the 
                         performance of the forecasts, showed that the ANN was superior to 
                         MLR in most seasons. When compared with Eta Model forecasts, it 
                         was observed that the ANN has a tendency to forecast moderate and 
                         high rainfall with greater accuracy during the austral summer. 
                         Also, when the observed rainfall of the previous day is included 
                         as a predictor, the TS showed the best performance in continuous 
                         rain and well-organized meteorological systems. On the other hand, 
                         in the austral winter period, characterized by slight rain, the 
                         ANN showed better forecasting ability than did the Eta Model. The 
                         obtained results also suggest that in the austral winter rainfall 
                         is more predictable because convection is less frequent, and when 
                         this occurs the forcing is dynamic instead of thermodynamic.",
           copyholder = "SID/SCD",
                  doi = "10.1175/WAF981.1",
                  url = "http://dx.doi.org/10.1175/WAF981.1",
                 issn = "0882-8156",
             language = "en",
           targetfile = "Valverde Ramirez_Linear.pdf",
        urlaccessdate = "28 abr. 2024"
}