Long-term seasonal rainfall forecasting: efficiency of linear modelling technique

Abstract

Using the lagged (past) climate indices, including El Nino–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) as input parameters and long-term spring rainfall as outputs, calibration and validation of the linear multiple regression (MR) models have been performed. Since Australian rainfall varies both temporally and spatially, the analysis on the linear MR models was performed on regional scale. These models show the capability of linear MR technique for long-term predictions of Western Australian spring rainfall. The emphasis was given to assess the statistical correlations between Western Australian spring rainfall and dominating large-scale climate modes. The efficiency of linear modelling technique was evaluated to predict seasonal rainfall forecasting. At the same time, the Pearson correlation (R), mean absolute error, root-mean-square error and Willmott index agreement (d) were used to assess the capability of MR models. The models which fulfilled the limits of statistical significances were used for the prediction of future spring rainfall using independent data set. The results indicate that during calibration periods maximum achievable correlations varied from 0.47 to 0.53 for the selected stations. In regard to predict peaks and troughs of rainfall time series, it was found that correlations between predicted and actual peaks varied from 0.82 to 0.94 and between predicted and actual troughs varied from 0.53 to 0.91.