OVER the last year, John Abbot and I have worked on a new method for forecasting rainfall based on the use of artificial intelligence.
We have benchmarked our forecasts against output from the Australian Bureau of Meteorology’s General Circulation Model (POAMA 1.5). Our model gives a more accurate forecast for 16 of the 17 sites in Queensland that have the highest quality rainfall data. Our methodology is detailed in a new paper that will soon be published in the international journal Advances in Atmospheric Sciences.[1]
Artificial neural networks can find existing complex relationships and patterns repeated in rainfall data. The neural network software that we run on our standard laptop computer, can perform millions of calculations very quickly, and in this way find natural patterns that repeat themselves.
In contrast, the Bureau uses general circulation models; the same models used to predict global warming. These models attempt to forecast seasonal and monthly rainfall by working from a particular theory of climate. Our system is radically different in that we let the model find the patterns. We let the model find the natural cycles and then forecast forward up to 9 months.
That our system works, proves that there are patterns – natural recurrent cycles – in historical rainfall data.
Contrary to some of the claims of some climate sceptics: the natural hydrological cycle is not totally chaotic.
Contrary to the claims of most warmists: these natural rainfall cycles have not been perturbed by the current elevated levels of carbon dioxide.
What we did is tune one of the most advanced off-the-shelf software packages for pattern recognition (Synapse, Peltarion) into the natural patterns and recurrent cycles in historic rainfall data.
We found a combination of historic rainfall and temperature data combined with historic data for three climate indices (Southern Oscillation Index, Pacific Decadal Oscillation and Nino 3.4) gave the most accurate forecasts. Inclusion of solar irradiance and sunspot number did not enhance the performance of our model. Inputting carbon dioxide concentrations doesn’t improve our model.
We believe we have barely scratched the surface in terms of potential for rainfall forecasting using this method for eastern Australia and are looking for significant additional funding to progress this work.
The work has so far been wholly funded by the B. Macfie Family Foundation and supported by the Centre for Plant and Water Science at Central Queensland University.
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[1] Email me at jennifermarohasy at jennifermarohasy.com if you would like an advance copy of our paper:
John Abbot and Jennifer Marohasy
Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia
Advances in Atmospheric Sciences, Volume 29, Number 4, Pages 717-730.
[Read more…] about Using Artificial Intelligence to Forecast Rainfall
Jennifer Marohasy BSc PhD has worked in industry and government. She is currently researching a novel technique for long-range weather forecasting funded by the B. Macfie Family Foundation.