@Article{MendesMare:2010:CoArNe,
               author = "Mendes, David and Marengo, Jos{\'e} A.",
          affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "Temporal downscaling: a comparison between artificial neural 
                         network and autocorrelation techniques over the Amazon Basin in 
                         present and future climate change scenarios",
              journal = "Theoretical and Applied Climatology",
                 year = "2010",
               volume = "100",
               number = "3-4",
                pages = "413--421",
                month = "may",
             keywords = "seasonal cycle, South America, trends, preciptation, 
                         variability.",
             abstract = "Several studies have been devoted to dynamic and statistical 
                         downscaling for both climate variability and climate change. This 
                         paper introduces an application of temporal neural networks for 
                         downscaling global climate model output and autocorrelation 
                         functions. This method is proposed for downscaling daily 
                         precipitation time series for a region in the Amazon Basin. The 
                         downscaling models were developed and validated using IPCC AR4 
                         model output and observed daily precipitation. In this paper, five 
                         AOGCMs for the twentieth century (20C3M; 1970-1999) and three SRES 
                         scenarios (A2, A1B, and B1) were used. The performance in 
                         downscaling of the temporal neural network was compared to that of 
                         an autocorrelation statistical downscaling model with emphasis on 
                         its ability to reproduce the observed climate variability and 
                         tendency for the period 1970-1999. The model test results indicate 
                         that the neural network model significantly outperforms the 
                         statistical models for the downscaling of daily precipitation 
                         variability.",
                  doi = "10.1007/s00704-009-0193-y",
                  url = "http://dx.doi.org/10.1007/s00704-009-0193-y",
                 issn = "0177-798X",
                label = "lattes: 4411895644401494 1 MendesMare:2009:CoBeAr",
             language = "en",
           targetfile = "temporal.pdf",
        urlaccessdate = "15 jun. 2024"
}