What question did this study set out to answer?

To improve the skill of seasonal rainfall predictions in Australia using optimal linear combinations of forecasts.

February 5, 2026

Optimal linear combination of seasonal forecasts

Puntos clave

To improve the skill of seasonal rainfall predictions in Australia using optimal linear combinations of forecasts.
Applied linear combination technique to SOI-based forecasts.
Utilized linear discriminant analysis and SOI phase analogue techniques.
Achieved optimal weighting through least-squares variational reduction of forecast errors.
Significant improvement in forecast skill over a twenty-year period (1972-1991).
Combined forecast reduced mean square errors to a half-Brier score of 0.197.
Individual models had half-Brier scores of 0.213 and 0.221 before combination.

Resumen

This paper illustrates the improvement in skill obtained in seasonal rainfall outlooks for the Australian region from the optimal combination of partially independent statistical prediction schemes. The linear combination technique was applied to two Southern Oscillation Index (SOI) based seasonal rainfall forecast techniques currently used in the National Climate Centre; a linear discriminant analysis (LDA) technique and a SOI phase analogue technique. Optimal weighting of the individual probability predictions of each prediction scheme is achieved using a least-squares variational reduction of the forecast errors with respect to the weighting factors. The combination of the two prediction schemes shows a significant improvement in forecast skill over a twenty-year evaluation period from 1972 to 1991. Mean square errors (half-Brier scores) of 0.213 for the SOI phase technique and 0.221 for the LDA model were reduced to a half-Brier score of 0.197 for the combination over the evaluation period.

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