"The Blizzard of 2016" and predictability: Part I--the value of prediction

Mark Twain famously quipped, “Everybody talks about the weather but nobody does anything about it.” But these days, that’s far from accurate.  At least, an army of specialists try to predict the weather so that we can be prepared for it.  The various media, as well as governmental agencies, publicize forecasts.  But how good are those forecasts?

As a former meteorologist myself (back–way back–when I was an Air Force weather officer), I take an interest, partly professional but also conceptual, in how accurate forecasting has become in our computer and satellite era.

Last week, a storm developed over the southwest, and combined with atmospheric disturbance barreling down from the Canadian arctic, to cause huge rain and wind damage across the south and then veered north where it turned into “The Blizzard of 2016″, dubbed by the exaggeration-hungry media.  How well was it forecast and did that do any societal good?

Here is a past-few-days summary page of mapped conditions at upper air (upper left), surface (upper right) and other levels.  On a web page called eWall ( http://mp1.met.psu.edu/~fxg1/ewall.html ) you can scroll these for the prior 5 days.  The double Low pressure (red L‘s) on the right panel represent the center of the storm, steered in part by the winds aloft (other panels).

If you followed the forecasting over the week leading to the storm’s storming up the east coast to wreak havoc there, you would say it was exceedingly well forecast, and many days in advance. Was it worth the cost?  One has to say that probably many lives were saved, huge damage avoided, and disruption minimized: people emptied grocery store shelves and hunkered down to watch the Weather Channel (and State College’s own Accuweather).  Urgent things, including shopping for supplies in case of being house-bound, were done in advance and probably many medical and other similar procedures were done or rescheduled and the like.  Despite the very heavy snowfall, as predicted, the forecast was accurate enough to have been life-saving.

Lots of people still don’t do anything about it!
And yet….
Despite a lot of people talking about the weather, on all sorts of public media, masses of people, acting like Mark Twain, don’t do anything about it, even with the information in hand.  At least 12 people died in this storm in accidents, and others from coronaries while shoveling, and this is just what I’ve seen in a quick check of the online news outlets.  Thousands upon thousands were stranded for many hours in freezing cold on snow-sodden highways.  There were things like 25-mile-long stationary lines of vehicles on interstates and thousands of car and truck accidents.  That’s a lot of people paying the price for their own stubbornness or ignorance.  This is what such a jam looks like:

People were warned in the clearest terms for days in advance.  Our fine National Weather Service, in collaboration with complementary services in other countries, scoped out the situation and let everyone know about it, as is their very important job.  Some states, like New York,  properly closed their roads to all but necessary traffic. Their governments did their jobs.  Other states, like Kentucky, failed to do that.  So then, how is it that there was so much of what seems like avoidable damage?

Let’s put the issue another way: My auto insurance rates will reflect the thousands of costly claims that will be filed because of those who failed to heed the warnings and were out on the highways anyway. So I paid for the forecasts first through my taxes, and then through the purchase prices of goods whose makers pay to advertise on weather channels, but then I also have to pay for those whose foolhardiness led to the many accidents they’ll make claims for.  That’s similar to people knowingly enjoying an unhealthy lifestyle, and then expecting health insurance to cover their medical bills–that insurance, too, is amortized over the population of insured including those who watch their lifestyles conscientiously.  That’s the nature of insurance.

Some people, of course, simply can’t stay home.  But many just won’t.  Countless truckers were stranded on the roads.  They surely knew of the coming storm.  Did commercial pressure keep them on the road?  Then shame on their companies!  They surely could have pulled over or into Walmart parking lots to wait out the snowfall and its clearance–a day or so, say.  Maybe there aren’t enough parking lots for that, but surely, surely they should not have been on the Interstates!  And while some people probably had strong legitimate reasons for being out, and a few may not have seen the strong, repeated forecasts over the many preceding days, most and I would say by far the most, just decided to take their trips anyway.

Nobody can say they aren’t aware of pileups, crashes, and hours-long stalls that happen on Interstates during snowstorms.  It is not a new phenomenon!  Yet, again, we all will have to pay for their foolhardiness.  Maybe insurance should refuse to cover those on the road for unnecessary trips. Maybe those who clog the roads in this way should be taxed to cover the costs of, say, increased insurance rates on everyone else or emergencies that couldn’t be dealt with because service vehicles couldn’t get to the scene.

The National Weather Service, and companies who use their data, did a terrific job of alerting people of the coming storm, and surely saved many lives and prevented damage as a result.  Just as they do when they forecast hurricanes and warn of tornadoes.  Still, there are always people who ignore the warnings, at their own cost, and at cost to society, but that’s not the fault of the NWS.

But what about predictability? Did they get it right?  What is ‘right’?
It is a fair and important question to ask how closely the actual outcome of the storm was predicted.   The focus is on the accuracy in detail, not the overall result, and that leads one to examine the nature of the science and–of course in our case here on this blog–to compare it with the state of the art of epidemiological, including genetic, predictions.  Not all forecasts are as dramatic and in a sense clear-cut as a major storm like this one.

I have been in the ‘prediction’ business for decades, first as a meteorologist and subsequently in trying to understand the causal relationships, genetic and evolutionary, that explain our individual traits.  Tomorrow, we’ll discuss aspects of the Big Storm’s forecasts that weren’t so accurate and compare that with the situation in these biological areas.