By Olivia
Humes, Grade 11, Imagineers
Imagine
it’s the late 18th century. You’re in the backwoods of Pennsylvania, which has
just recently become a part of the world’s newest country--the United States.
Since you live in a settlement many miles away from Philadelphia, your income
and success this year completely depends upon your harvest, which in turn,
depends upon the weather. But how do you know if you’ll get enough sunshine or
rain to produce a good harvest? How do you know when the next storm will blow
in?
The short
answer is, you don’t.
Professor
Cliff Mass is somewhat of a weather celebrity in the Pacific Northwest. He runs
a weather blog, a radio show, and creates cutting-edge weather forecasts at the
University of Washington. I got the opportunity to listen to him lecture on the
history and future of mathematical weather forecasting last Sunday, April 14th.
He
explained that in the early days, weather predictions were qualitative
rather than quantitative. Sayings like “red skies at morning, sailors
take warning” or “clear moon, frost soon” were often used to predict the
weather of the next few days. Though many of these sayings reflect the cause
and effect relationships of the underlying processes that change our weather,
they offer little specific predictions, and only allow general actions to be
taken. The first hint that quantitative weather prediction could be used to
predict future conditions came when Benjamin Franklin, our most
meteorologically inclined Founding Father, discovered that storm systems moved
in a roughly west-to-east pattern across the country. But for a long time, the
precision of our early instruments and the availability of data limited the
effectiveness of weather prediction.
To a
large extent, these problems still exist today. Even with computational models
developed in the 1920’s using physical equations, such as laws governing the
behavior of gases, and the almost unimaginable computing power available
literally at our fingertips today, the accuracy of our atmospheric predictions
aren’t quite on point. We’ve accepted this as part of everyday life, despite
the confident forecasts delivered to us via our local news stations and
smartphones. A lack of weather reporting stations in and over the Pacific Ocean
can have a big effect on the accuracy of our reports, particularly on the West
Coast, where storm systems arrive directly from over the water.
But even
with perfect global weather coverage and infinite computing power, weather
prediction is inherently difficult. Feedback loops in the atmosphere, where
individual processes either enhance or reduce each other’s effects, create a
chaotic, nonlinear system where the smallest inaccuracies in measurement can
dramatically affect the outcome of a whole day’s weather.
These
inaccuracies can be as small as observational errors intrinsic to the equipment
we use. This principle is known as the butterfly effect, but Professor Mass
likens it to a pinball game. Imagine a pinball game, but one in which you have
no control over the paddles, and can only pull back the spring at the beginning
of the game to start the ball’s motion. No matter how accurately you try to
recreate a shot that resulted in a huge score, you’re extremely unlikely to
recreate that lucky shot if your friends ask you to prove it to them later. The
weather, like the pinball game, is a similarly chaotic system.
The
approach that Mass takes with his predictions, he explained, is to run his
weather models over and over, varying the initial conditions slightly, within
observational error. Then, each result is averaged, and a general forecast of
probabilities is created. These forecasts prove to be both more accurate and
more useful to us, and allow us more realistic expectations of the next day’s
weather. After all, how do weather forecasters know when the temperature will
be exactly 70°?
Now
imagine it’s 2013 again. Imagine you’ve just taken out your phone to check the
weather reports. Looks like there’s a 75% chance temperatures will be between
80 and 85 degrees. Put on some sunscreen. It’s time to hit the beach!
Read
Professor Cliff Mass’s blog here:
Happy Earth Month!
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