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By Ria Persad, CEO of StatWeather

Have you ever wondered how accurate the hurricane forecasts are that you see in the media and on television? A meteorologist can have a very plausible-sounding scientific explanation to their forecast, but what really counts to the general public is, “How accurate is this? What’s your track record? Should I believe you?”

This article tracks the accuracy of pre-season tropical and hurricane forecast numbers.  For example, when a forecaster says that it will be an “above average” or “very active” hurricane season and gives you the expected number of tropical storms, hurricanes, and major hurricanes for the upcoming season, how far off are these numbers compared to what actually transpired?

I turned to the Reference source page data from Wikipedia’s entries on “2013 Atlantic Hurricane Season”, “2012 Atlantic Hurricane Season”, and so on, assimilating 10 years’ worth of hurricane forecasts from a total of 7 weather forecasters:  NOAA, Colorado State University, TSR at University College London, North Carolina State University, UK Met Office, Florida State University, and WSI / The Weather Company.

When a weather forecaster presented a range of values (e.g., NOAA predicts 8-10 hurricanes), I took the average of the range in my error analysis.  I also used the forecasts that were released closest to June 1st, which is the official start of the Atlantic hurricane season and also the time when many industries are making preparations for the upcoming season.

The table below reflects how far off forecasters have been in predicting the numbers of tropical storms, hurricanes, and major hurricanes over the last 10 years (2004 to 2013).  For example, the table shows that on average, over the last 10 years, NOAA’s Forecasted Count for Tropical Storms was off by plus or minus 4.

In the table, the 50-Year Average and the 30-Year Average are tracked as if they were “forecasters.”  In other words, if you just went with a 50-Year Average or a 30-Year Average, would it have been closer to what actually happened, as opposed to going with a “real” weather forecaster? Smaller numbers in the table represent greater accuracy.



CO State Univ.

Consensus of 7 Forecasters

50-Year Average (1950-2000)

30-Year Average (1981-2010)

Forecasted Number of TROPICAL STORMS (Average Absolute Error)






Forecasted Number of HURRICANES (Average Absolute Error)






Forecasted Number of MAJOR HURRICANES (Average Absolute Error)






The major takeaways from the data are

1)   The weather forecasters above are better than going with a climatological average when predicting the number of Tropical Storms (winds up to 73 mph).

2)   The weather forecasters above are about the same as going with a climatological average when predicting the number of Hurricanes (winds above 74 mph, Category 1 or 2).

3)   The weather forecasters above are about the same as going with a climatological average when predicting the number of Major Hurricanes (winds above 111 mph, Category 3, 4, or 5).

A consensus of forecasters can be slightly better than going with one forecaster alone.  But if the consensus are all thinking along the same lines, the advantage is very small indeed.

What I have learned from this data is that conventional hurricane forecasting science has gotten us “ahead of the curve” when anticipating the number of tropical storms in a given season, with clear skillfulness.

These methods do not get us “ahead of the curve” when it comes to predicting what proportion of tropical storms will then develop into hurricanes and major hurricanes.  For example, if you were to go with a hurricane climate average instead of the weather forecasters above, you’d have a 50-50 chance of beating the weather forecaster.  According to my battery of tests, weather forecasters were better than hurricane climate averages for 5 out of the past 10 years.  It is accurate to say that when it comes to predicting whether a season will have more or less hurricanes, your guess is as good as the weather forecaster’s.

The science of hurricane development and intensification has been tackled by organizations who use non-traditional methods of statistics and machine learning, astrophysics, dynamical systems, and physical oceanography. My company, StatWeather, employs some of these non-traditional methods which have, thus far, “beat the curve” at the expense of going against the grain and being named contrarian. In 2013, StatWeather publicly forecasted a below normal hurricane season, a minority viewpoint, which verified.  In 2012, StatWeather forecasted a strong hurricane season, again a minority viewpoint, which verified. In 2014, StatWeather is forecasting an active hurricane season, which is again regarded as contrarian.

Many 2014 hurricane forecasts are heavily dependent upon El Niño development.  Over the last 10 years, El Niño episodes have coincided with hurricane activity ranging from above normal to below normal activity.  StatWeather’s position is that El Niño is but one ingredient in the pie, and is not always the overriding component.

If you would like to experience long-range weather forecasts that will get you ahead of the curve, visit www.statweather.com or email service@statweather.com.

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