Monthly and Seasonal Rainfall Forecasting in Southern Brazil Using Multiple Discriminant Analysis

Abstract

multiple discriminant analysis was employed to forecast monthly and seasonal rainfall in southern Brazil. The methodology used includes six steps: data acquisition, preprocessing, feature extraction, feature selection, classification, and evaluation. The predictors (atmospheric, surface, and oceanic variables) and predictand (rainfall) were obtained from the Twentieth Century Reanalysis (version 2), as well as from the HadISST1 (Met Office Hadley Centre) and Global Precipitation Climatology Centre (GPCC) databases. The definition of key regions (feature extraction step) was performed using spatial principal component analysis. In the selection step, the rainfall time series were allocated into terciles, which were related to the predictors via multiple discriminating analyses. The results revealed that ? of the predictors are associated with atmospheric pressure and also emphasized the role of atmospheric circulation over the Antarctic region and its surroundings. Surface variables (albedo and soil moisture) were also of great importance in the forecasting. The average skill score (gain over climatology) was 29%. It is concluded that the proposed model is a reliable alternative for use in forecasting monthly and seasonal rainfall over southern Brazil.