The author has previously presented work providing an analysis of the relative skill displayed by human predictions (the current official forecasts), and by automated forecasts generated by a knowledge based system. The work suggested that adopting a strategy of mechanically combining human and automated predictions has the potential to lift the percentage variance explained by human predictions by 7.9%. Whilst encouraging, this suggestion needed to be validated by a fresh real-time trial conducted on a new set of independent data.

Forecast verification data from a new 100-day real-time trial, conducted from August to November 2005, on a fresh set of independent data, was analysed. For this new trial, the system was modified to mechanically combine human and automated predictions. The trial confirmed the previous work, the percentage variance explained being lifted by 7.7%.

In the light of the results of the 100-day trial, a number of minor modifications were made to the computer system (for example, from 27 December 2005, Day-1 wind forecasts were generated by a combining procedure; previously, they had been automated). The real-time trial was then continued. After 300 Day-1 to Day-7 Melbourne forecasts from 20 August 2005 to 15 June 2006, inclusive, that is, 2100 individual predictions, the average lift in percentage variance of weather (rainfall amount, sensible weather, minimum temperature, and maximum temperature) explained was 8.1% over that explained by the current official forecasts (refer to Figure and Table), the addition of more independent data further affirming the result suggested by the earlier work.

There is increasing interest in the question of what might be the appropriate future role for the human in the forecast process. The results presented here show that automated forecasts are unable (by themselves) to fully replicate the decision making processes of human forecasters. Similarly, the results also show that human forecasters (by themselves) are unable to optimally integrate into the forecasting process, guidance from automated predictions.

Two or more inaccurate but independent predictions of the same future events may be combined in a very specific way to yield predictions that are, on the average, more accurate than either of them taken individually. This is because, when combined mechanically, each "brings to the table" a different knowledge set.

That the combined forecasts are more accurate than individual currently available predictions taken separately, provides companies involved in forecasting with a potential competitive advantage over their peers should they choose to adopt a strategy of mechanically combining predictions.

With automated forecasts unable to fully incorporate human forecasters' valuable domain and contextual knowledge, there should be a need for the human forecaster well into the future. This future role may be as an input to a system that mechanically combines human predictions with automated forecasts.