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ABOUT one week after Central Queensland University put out a media release announcing the publication of our latest paper on medium-term rainfall forecasting, John Abbot and I received a phone call from the ABC, from a rural reporter.

Part of the interview with Professor Abbot can be heard online here, http://www.abc.net.au/news/2014-02-21/nrn-forecast/5275122

ABC journalists Cassie Hough and Kathleen Calderwood have also written a short article based on the interview in which they effectively capture the key points, http://www.abc.net.au/news/2014-02-21/nrn-forecast/5275122

And following is a succinct summary of the interview by one of the media monitoring services:

“New research from the University of Central Qld shows medium-term rainfall forecasts may be more accurately predicted by a form of artificial intelligence, which aim to predict the weather through pattern detection, and predicts not what the weather will do day-to-day, but in a month or more. John Abbot, researcher, says he was motivated to do the research after the huge flood at the start of 2011.

Abbot talks about how his research works, and the problems with modern-day weather forecasting systems, and says farmers and mine-operators need accurate data as opposed to vagaries. Abbot says he would like the Bureau of Meteorology take more interest in what he is doing.”

The paper can be downloaded from Science Direct, with full open access available until March 26, here,
http://elsarticle.com/1ej97n3

[Open access until March 26, 2014]

Highlights include:

1. Artificial neural networks are developed to forecast monthly rainfall for localities in Queensland.

2. Comparative utility of input variables, including local maximum and minimum temperatures, is tested in the artificial neural network.

3. The Inter-decadal Pacific Oscillation, IPO, is found to have utility for rainfall forecasting.

4. Artificial neural networks forecasts are superior to Australian Bureau of Meteorology official seasonal and monthly forecasts.

I’ve just published in the peer-reviewed literature explaining:

“Much of the present research effort by government institutions in Australia, which is focused on monthly and seasonal rainfall forecasts, is limited to the application of general circulation models, in particular, POAMA. However, results so far have been disappointing with medium-term monthly forecasts consistently about equivalent to, or worse than, climatology, Table 4. Nevertheless in June 2013, POAMA was adopted as the system for generating the BOM’s official seasonal forecasts.”

http://www.sciencedirect.com/science/article/pii/S0169809513003141
[Open access until March 26, 2014]

In short, the Bureau could better serve the Australian public by just updating and publishing the long-term average rainfall each month for localities for which it has statistics, rather than pretending it can generate a reliable forecast. And I offer the same advice to the UK Met Office which yet again botched its seasonal weather forecast. The following quotes via Benny Peiser at The Global Warming Policy Foundation.

BUNGLING weather bosses predicted a drier than usual winter, it has emerged. The Met Office’s staggeringly inaccurate forecast was made at the end of November last year – just a month before the record-breaking deluge began. And the agency gave just a one in seven chance the three following months would “fall into the wettest category”. On Nov 21, its experts predicted: “For the December-January-February period as a whole, there is a slight signal for below-average precipitation”. The calamitous estimate emerged as Downing Street dubbed the devastation caused by the floods as “Biblical”. –Tom Newton Dunn, Political Editor, The Sun, 11 February 2014 [Read more…] about General Circulation Models Can’t Forecast Seasonal Rainfall