Thursday, November 30, 2017

Today Ended the 2017 Atlantic Hurricane Season

Lately, I've been letting out a bit more of a sigh of relief when hurricane season is over.  It also usually coincides with a second "rainy season" here.  Unlike the summer rainy season, which is more predictable in hitting during the afternoon, and more like every day, the winter rainy season is rain that comes in advance of a cold front.  Winter rain can be any time of day and more like one or two days a week.   

So how did it stack up against NOAA predictions?  NOAA did pretty well this time in this regard:

They got in the right range in number of named storms, and both Hurricanes and Major Hurricanes are off by the same storm.
Based on the Accumulated Cyclone Energy index, which measures the combined intensity and duration of the storms during the season and is used to classify the strength of the entire hurricane season, 2017 was the seventh most active season in the historical record dating to 1851 and was the most active season since 2005.
A couple of milestones occurred this season: the almost-12 year stretch without a major hurricane making landfall in the US was broken by Harvey in Texas.  All in all, three major hurricanes made landfall in the US.  Besides Harvey there was Irma here in Florida; and Maria on Puerto Rico. 

Watts Up With That reports lots of interesting little details from NOAA, but I'll leave that to those  interested.  My main concern is the quality of the forecasting and I'm of the opinion that they aren't much better than they were five or ten years ago.  No, I can't quantify that.  I don't have metrics to do so.  I posted some of my discomfort with the way forecasts are made back after Irma.  A month before that I examined one potential storm in particular, and how bad the long range forecasts on it were.

There's a very tough reality here.  From everything I can see, the hurricane center simply is incapable of forecasting the path accurately enough for us to evaluate the risk a couple of days out of the storm.  In the case of a storm like Irma, they're incapable of forecasting the path accurately enough for someone to decide to get out of the Florida peninsula from Miami area when they still can (two days in advance)

The winds to be avoided in a hurricane are within the first few miles around the eyewall.  Even in the strongest storms, hurricane force winds don't cover 50 miles, it's more like 10-30 miles.  We have evidence that hurricane conditions in Irma, when she crossed the Keys as a Cat IV storm, were on the order of 20 miles wide, including the eye.  We have evidence that when Irma crossed Naples it wasn't even a hurricane.  Hurricanes are chaotic, though, and it's very common for them to cause tornadoes as the winds sweep onshore.  There are many aspects that appear to be fundamentally unpredictable.

To borrow a quote from myself back in this August:
Long time readers may recall that last October, within 24 hours of closest approach, the NHC forecast Hurricane Matthew to be over my head as a Cat IV storm. Actual closest approach was about 50 miles away and a much weaker cat II. We didn’t get hurricane force winds. That’s an enormous difference in the risk from the storm, since wind damage scales as velocity squared.  I'd like to see them more accurate at 24 hours, let alone at 10 days.
I'll go easy on them.  At 48 hours out, I want them to peg the center to within +/- 5 miles.  It's not like they don't have the most advanced supercomputers known to man at their request, right?  Do you think they can do that by 2050? 


  1. This begs a more important question: If they miss a storm's intensity or location by a large margin just 48 hours out, how can I trust the same agency to predict world climate to resolution of 0.6 degC 100 years from now?

    1. ... 0.6 degC 100 years from now When the error bars are several times the predicted increase. The answer is "of course you can't", but you knew that.

  2. Are the paths and intensities of Pacific typhoons being forecast with greater accuracy?
    Again focusing on the Pacific, are the countries that have high risk from storms using computer models that are getting better results than the models used by NOAA? Japan would spring to mind.

    1. That's a tough question because it's remote and I don't speak any of the languages there. The questions here come from blogs and people with personal weather stations. The way to answer that would be to talk with those people or read their blogs in Japan or the Philippines.

      I don't know of any official viewpoint that questions our NHS forecasts, just us crank bloggers. It may be buried in the fine print of what the NHS says, but it's just my experience that these forecasts don't seem any more accurate than any other weather forecasts.

  3. Given the supercomputers, the multitude of models, and the years of refinement of those models, I agree that the inability to make accurate predictions is puzzling. It implies that the models are missing some key factor. One would think that every environmental factor is already included, so what are they missing? Is it a man-made factor such as HAARP, Chem-trails, or other weather modification experiments? I try not to get caught up in tinfoil hat stuff, but the lack of accuracy in predictions of hurricanes and everyday local weather forecasts is more than puzzling.


    1. My bet is that it's fundamentally unpredictable. Back when (mathematical) chaos was first being discussed, it was described as "sensitive dependence on initial conditions". Tiny differences in measured temperatures, pressures and so on make the system "blow up" and refuse to converge.

      There are big research groups here in the US, the UK and elsewhere that make elaborate models of the weather to predict hurricane paths. You can look at those model ensembles at any time; sometimes the tracks are close, sometimes they're not, but there's always some model (or two) that's extremely different from the others.

      Which says it doesn't matter what supercomputers they throw at it. What they need, if anything, is a finer resolution dataset. Higher precision measurements and more of them.