Seasonal to interannual rainfall probabilistic forecasts for improved water supply management: Part 2 — Predictor identification of quarterly rainfall using ocean-atmosphere information

This paper is the second in a series of three in the current issue that present a framework for long-term rainfall probabilistic forecasts. The first paper of the series presented the partial mutual information (PMI) criterion as an efficient basis for system predictor identification in the probabilistic forecast context. This paper presents applications of the PMI criterion to identify the best predictors of quarterly rainfall at Warragamba dam near Sydney Australia, using a range of ocean-atmosphere predictor variables. Two separate prediction scenarios are considered. The first scenario involves the use of three commonly used El Niño Southern Oscillation indices as predictors of the Warragamba dam quarterly rainfall. The second scenario uses sea surface temperature anomalies averaged over 5° latitude by 5° longitude blocks as the plausible system predictors. A reconstructed sea surface temperature anomaly dataset extended to 1856 is used in this analysis. The usefulness of the predictors from both scenarios is evaluated by forecasting rainfall for seasonal to interannual lead times, using a Generalised Additive Model. Details of the rainfall probabilistic forecasts using the predictors identified under the two scenarios are presented in the last paper of this three-paper series.