Category Archives: hurricane forecasting

Predicting the Hurricane Season

I’ve been focusing recently on climate change and extreme weather events, such as hurricanes and tornados. This focus is interesting in its own right, offering significant challenges to data analysis and predictive analytics, and I also see strong parallels to economic forecasting.

The Florida State University Center for Ocean-Atmospheric Prediction Studies (COAPS) garnered good press 2009-2012, for its accurate calls on the number of hurricanes and named tropical storms in the North Atlantic. Last year was another story, however, and it’s interesting to explore why 2013 was so unusual – there being only two (2) hurricanes and no major hurricanes over the whole season.

Here’s the track record for COAPS, since it launched its new service.


The forecast for 2013 was a major embarrassment, inasmuch as the Press Release at the beginning of June 2013 predicted an “above-average season.”

Tim LaRow, associate research scientist at COAPS, and his colleagues released their fifth annual Atlantic hurricane season forecast today. Hurricane season begins June 1 and runs through Nov. 30.

This year’s forecast calls for a 70 percent probability of 12 to 17 named storms with five to 10 of the storms developing into hurricanes. The mean forecast is 15 named storms, eight of them hurricanes, and an average accumulated cyclone energy (a measure of the strength and duration of storms accumulated during the season) of 135.

“The forecast mean numbers are identical to the observed 1995 to 2010 average named storms and hurricanes and reflect the ongoing period of heightened tropical activity in the North Atlantic,” LaRow said.

The COAPS forecast is slightly less than the official National Oceanic and Atmospheric Administration (NOAA) forecast that predicts a 70 percent probability of 13 to 20 named storms with seven to 11 of those developing into hurricanes this season…

What Happened?

Hurricane forecaster Gary Bell is quoted as saying,

“A combination of conditions acted to offset several climate patterns that historically have produced active hurricane seasons,” said Gerry Bell, Ph.D., lead seasonal hurricane forecaster at NOAA’s Climate Prediction Center, a division of the National Weather Service. “As a result, we did not see the large numbers of hurricanes that typically accompany these climate patterns.”

More informatively,

Forecasters say that three main features loom large for the inactivity: large areas of sinking air, frequent plumes of dry, dusty air coming off the Sahara Desert, and above-average wind shear. None of those features were part of their initial calculations in making seasonal projections. Researchers are now looking into whether they can be predicted in advance like other variables, such as El Niño and La Niña events.

I think it’s interesting NOAA stuck to its “above-normal season” forecast as late as August 2013, narrowing the numbers only a little. At the same time, neutral conditions with respect to la Nina and el Nino in the Pacific were acknowledged as influencing the forecasts. The upshot – the 2013 hurricane season in the North Atlantic was the 7th quietest in 70 years.

Risk Behaviors and Extreme Events

Apparently, it’s been more than 8 years since a category 3 hurricane hit the mainland of the US. This is chilling, inasmuch as Sandy, which caused near-record damage on the East Coast, was only a category 1 when it made landfall in New Jersey in 2012.

Many studies highlight a “ratchet pattern” in risk behaviors following extreme weather, such as a flood or hurricane. Initially, after the devastation, people engage in lots of protective, pre-emptive behavior. Typically, flood insurance coverage shoots up, only to gradually fall off, when further flooding has not been seen for a decade or more.

Similarly, after a volcanic eruption, in Indonesia, for example, and destruction of fields and villages by lava flows or ash – people take some time before they re-claim those areas. After long enough, these events can give rise to rich soils, supporting high crop yields. So since the volcano has not erupted for, say, decades or a century, people move back and build even more intensively than before.

This suggests parallels with economic crisis and its impacts, and measures taken to make sure “it never happens again.”

I also see parallels between weather and economic forecasting.

Maybe there is a chaotic element in economic dynamics, just as there almost assuredly is in weather phenomena.

Certainly, the curse of dimension in forecasting models translates well from weather to economic forecasting. Indeed, a major review of macroeconomic forecasting, especially of its ability to predict recessions, concludes that economic models are always “fighting the last war,” in the sense that new factors seem to emerge and take control during every major economic crises. Things do not repeat themselves exactly. So, if the “true” recession forecasting model has legitimately 100 drivers or explanatory variables, it takes a long historic record to sort out the separate influences of these – and the underlying technological basis of the economy is changing all the time.