An ensemble forecast of semi-arid rainfall using large-scale climate predictors

A case study examining ensemble forecasts of semi-arid seasonal precipitation is presented. The focus is on computing an appropriate correlation between large-scale climate predictors and seasonal precipitation over a long-term forecast period (1967-2009) for a semi-arid catchment in Iran. Potential predictors of the dominant precipitation modes were identified from several large-scale climate features using principal component analysis. Linear regression together with two nonlinear models, the adaptive neuro-fuzzy inference system (ANFIS) and the multi-layer perceptron, was applied to forecast seasonal ensemble precipitation time series. The analysis suggests that seasonal precipitation is statistically aligned with the predictor's variability. An ensemble forecast of spring precipitation modes showed a stronger correlation with the preceding season (winter predictors) in the ANFIS algorithm. The potential effect of climate predictors during the spring may lead to severe and longer hydrological extremes especially when they are out of phase or coincident. These results highlight that skilful prediction of semi-arid spring precipitation may be possible using winter predictors and a nonlinear ensemble forecast model.