Neural networks in weather forecasting

Using Artificial Intelligence to Forecast Rainfall

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.

Source: Jennifer Marohasy

LAP LAMBERT Academic Publishing Applications of Neural Network in Long Range Weather Forecasting: In the context of smaller geographical region (i.e., Chhattisgarh State, India)
Book (LAP LAMBERT Academic Publishing)

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