Abstract
Abstract This study expands our earlier climate prediction work for Brazil's Nordeste to develop methods of forecasting the March–June precipitation with differing lead times by exploring the potential of various data sources and options of information extraction. Observations include indices of Nordeste rainfall, an index of sea surface temperature (SST) in the equatorial Pacific, and the fields of meridional wind component and SST in the tropical Atlantic. Empirical orthogonal function (EOF) analysis was applied to construct indices of the meridional wind component and SST. These series formed the input to stepwise multiple regression models, an experimental neural network model, as well as to linear discriminant analysis. The dependent dataset 1921–57 (excluding 1943–47) was used for the method development, while the independent dataset 1958–89 was reserved for prediction. Of primary interest is the prediction of March–June rainfall from information through January. A new SST dataset with improved qual...
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