Hey there everyone, welcome to Seeker+,
where we pick one topic and hyperfocus on it. I’m your host Julian Huguet. And I want to ask you,
have you ever packed for a long trip? You know, you checked the weather forecast wherever you're going
and you brought the appropriate wardrobe. Your suitcase is full of Hawaiian shirts and short
shorts that really show off the goods, and then a week into the trip, you're caught
in a snowstorm and cursing the Weather Channel. Has that ever happened to you?
Ok maybe not as much since 2020, what with the pandemic shutting down everyone’s vacation plans,
which incredibly, actually made weather forecasts worse. Apparently meteorologists
use data from commercial flights to help predict weather, and since people weren’t traveling, they
didn’t have as much data to work with, which was something I had no idea about.
In fact, I didn’t know a lot about the weather, so for this series I wanted to explore it.
We’re going to unpack everything about the weather, why it seems the forecast is
always wrong, what it takes to maybe control the weather, and the big enchilada: terraforming
other planets. But first, let’s dig into why meteorologists seem to get the weather wrong so
often and still manage to keep their jobs. I mean can you imagine if you were allowed that much leeway at your work!
Really though, we should be giving these people a break. I know,
I know, controversial. Hot take. But forecasting the weather is pretty hard stuff, to put it
mildly. Meteorologists have to look at real-time data from numerous sources like weather balloons,
buoys, radar, and satellites to make predictions about what might happen next, and even with all
that data there’s always some uncertainty. The weather is inherently unpredictable and
meteorologists try to provide order for that chaos. In fact, a key principle in chaos theory,
the “butterfly effect,” has its origins in weather forecasting. Now, I know when I say chaos theory you
think of Jurassic Park and Jeff Goldblum and not the weather but hey, sometimes real
life just isn’t as sexy as T-rexes and Dr. Ian Malcolm. I mean what is as sexy as Ian Malcolm?
Sorry I was getting lost in a daydream for a bit. The term “butterfly effect” though was coined in the 60s
by meteorologist Edward Lorenz. The idea goes that small changes to initial conditions can have large
consequences down the road. The saying you’re probably familiar with is if “a butterfly flaps its wings
on one side of the world, it can cause a hurricane on the other side,” but that’s not how the effect got
its name. It actually relates to the resulting shape of a graph that Lorenz made when he plotted points to an equation
that represented the motion of gas. These plotted points kind of look like a butterfly’s
wings and they show that small changes can make big differences, but the outcomes aren’t totally
random. Whatever happens still has to be within the realm of possibility, so ironically, that whole
butterfly causing a hurricane schtick is actually not a possible outcome of the butterfly effect,
at least I’m pretty sure. But you’d have to ask a meteorologist to double check. Anyway, this graph,
by the way, is called a strange attractor, which is my signature move when I go to a bar. Lorenz
came up with this graph when he was working on an experiment to model a weather prediction
and he rounded off one number at the starting point of the model. He found that this minor change
could result in anything from sunny skies to violent storms. And Lorenz concluded that the further
out you try to predict the weather, the more unreliable the forecast becomes. Even 60 years
later, predicting a 10-day forecast—or longer—is still only right about half the time. Now that may
be one reason why meteorologists get blamed for getting the weather wrong so much of the time, but
there’s more to the story, and sometimes it comes down to which allegiance meteorologists swear to…
I’m kinda exaggerating, but not really. There are two choices and meteorologists have to pick one.
I mentioned weather models earlier, right? Well today, meteorologists largely rely on two
major modeling systems: the American model and the European model. Both run on some of
the fastest supercomputers in the world that handle tons of variables for things like
temperature and pressure, and gather millions of measurements to help handle the initial conditions
of the atmosphere. Between the two of them, most forecasters actually agree that the European
system is a tad more accurate, mainly because it has a more powerful supercomputing system
and can really focus on medium-range forecasts. Those are the forecasts between 3-7 days.
Let’s look at a famous example in 2012 when the two systems were both used. The European model
accurately predicted Hurricane Sandy’s path westward more than a week in advance, but the
American counterpart predicted it would move east, away from the coast. And the people on America’s
East Coast will tell you exactly where that hurricane landed. Now this wouldn't be a good science show
if I gave you a sample size of one, so let's look at a few other examples. In 2015, the European
model accurately predicted that Hurricane Joaquin would miss the East Coast, and then in 1993, it correctly
forecast a Nor’easter, which was called the “Storm of the Century”. So probably a good thing it got that one
right. I think you get the point. Time and again, the European model outperforms the American model.
When these two model systems differ, forecasters just have to go with their gut. If it were me, my gut,
I would say every time that it was going to be cloudy with a chance of meatballs, but man I’m hungry, I haven’t had lunch yet.
Making accurate weather predictions also comes down to the location of weather stations. Cities
often have more stations than rural places, which mean more data to use for those weather models
compared to the sparsely populated areas. So forecasters might be predicting weather over the
same size area, but for cities they’re working off of more data than they are for rural locations. As Edward
Lorenz showed in his work on chaos theory, the outcomes are very sensitive to initial conditions,
so what might seem like a slight change or just a little less data
at the beginning can generate very different results in the predicted weather.
Now that’s not necessarily a deal breaker for those living outside of cities.
I just told you a whole bunch of reasons why predicting the weather isn’t as accurate as
we’d all like it to be. We can’t collect certain data when planes get grounded,
there are unpredictable systems constantly in play, there's competing computer models,
and a finite number of weather stations and data. It sounds like a Sisyphean task to me,
but meteorologists still press on despite working in uncertain territory. I think really we should be more
thankful for the forecasts we do have are as accurate as they are, and if you’re not,
I have a little secret for you. Five and seven day forecasts are actually right the vast majority
of the time, like 80 to 90 percent of the time. You heard that right, I’m actually defending
your local weather guy. They’re an easy target, but it’s not their fault that the future beyond
the next couple days is inherently a chaotic mess of math and hailstorms.
Even so, their forecasts are actually getting better all the time. In fact, today’s six-day
forecast is about as accurate as a five-day forecast ten years ago. And ten years from now,
our seven-day forecast might be as accurate as our current six-day forecast, and so on and so forth.
While I’ve just told you that the forecast is pretty reliable and accurate now,
there are still going to be some people out there who just don’t agree, and some of that might be down to a
misunderstanding about how to interpret those forecasts. Have you ever had your weather app
tell you there was a 30% chance of rain, so you bring your umbrella and galoshes just in case
and yet hour after hour, lo and behold, beautiful and clear sunny skies, no rain in sight.
But what if it did rain five kilometers away at your friend's house? The forecast was right for
your friend, but not for you. It all depends on a person’s point of view and a little case of
NIMBY-ism, or “not in my backyard-ism”, but with weather and not like big industrial projects.
No feathers ever seem to get ruffled when the temperature is off a few degrees and how often are
you staring at a thermometer to double check that it exactly matches the forecast? Really the pain
and anguish always seem to come when it involves rain or snow. That makes sense, it really does.
If you were told there was a 20 percent chance of snow and it snowed a lot, but you didn’t have
your galoshes on, then you might be upset. You might call the meteorologist mean names,
least of which was wrong. But if you knew how a chance of precipitation was calculated, you
may feel differently. The U.S. National Weather Services defines the probability of precipitation
as the chance of rain or snow occurring at any one spot in the area covered by the forecast.
Meteorologists use an equation which helps put that into perspective for you. It’s P = C x A,
or the probability of precipitation equals the meteorologist’s confidence that it will
rain (that’s the C) times the percentage of the area that is expected to get rainfall.
So if a meteorologist is 50% confident that 100% of your neighborhood will get rain, then there’s a
50% of rainfall. Conversely, if they think there’s a 100% chance that 50% of your neighborhood will
get rain, that will still show up on your weather app as a 50% chance of rain. And to make it even
more complicated, meteorologists can’t agree that this is how the percentage should be calculated.
One meteorologist actually says the probability of precipitation “measures the likelihood that you’ll
see rain at any given point within the forecast area within a 12 hour time period.” It’s a slight
difference, but the difference matters. Also, if you live somewhere that has micro climates,
a place where the weather can be drastically different from just one part of town to the next,
like say San Francisco—interpreting your forecast can get a little dicey. It’s all much more
complicated than the little symbols and numbers on your weather app might lead you to believe.
You could even argue if you wanted to get hyberbolic that meteorologists are playing
a high stakes game of roulette or poker or craps or whatever your game of choice is.
Yes, they’ve got supercomputers, years of fine tuned weather models, and millions
of measurements that get fed into those models, that’s all working on their side,
but there’s still one important consideration. Both weather forecasts and high stakes gambling
require a very refined sense of interpretation, that's aged and polished with experience, so
when meteorologists spot a storm ahead, they can confidently listen to their gut.
But could they predict the weather if someone was controlling it? We’ll get
into that in the next episode, but for now, we’re going to end part 1 there.
Next time you want to trash the weather forecast though, think again. There really is
so much that goes into something we take for granted every day and more often than not,
it’s actually right. But if there is a scientific topic or puzzling question that you want to know
more about, well we probably do too. Let us know some of your questions in the comments,
I promise we’re putting it into a big list and picking our favorites, so be sure to
subscribe if we cover something you suggest, and I’ll see you for the next Seeker Plus.