Improvements in precipitation simulation over South America for past and future climates via multi-model combination
Combining individual forecasts is one of the practices used to improve weather prediction results. Identifying which combination of techniques results in a more accurate forecast is the subject of many comparative studies as well proposals for combined methods. Here we compare three combination techniques: (1) principal component regression (PCR), (2) convex combination by mean squared errors (MSE) and (3) ensemble average to combine six regional climate models of the Regional Climate Change Assessment for the La Plata Basin Project (CLARIS-LPB) for variable rainfall in three regions: Amazon (AMZ), Northeastern Brazil (NEB) and La Plata Basin (LPB), for the past (1961-1990) and future (2071-2100) climates. The results indicate that the average RMSE values showed improved representation of climate for LPB in some months, which is an important advance in climate studies. On the other hand, PCR presented greater accuracy (lower RMSE) than MSE in the AMZ and NEB regions. In winter months, both combinations presented lower RMSE results, mainly PCR in the three study regions. The correlation coefficient supports the results already found, namely, PCR obtained moderate to strong correlations, which were statistically significant at 5 % in both regions for all months, while MSE presented low to moderate correlations, which were statically significant at 5 % only in some months. Based on that, PCR achieved the best corrected forecast, as it was superior in forecasting precipitation due to the lower RMSE value. It is noteworthy that the PCR data were first subjected to principal component analysis (PCA) and the scores were used to perform the prediction.