From the intersection of computational science and technological speculation,
with boundaries limited only by our ability to imagine what could be.
LILY’S EYES SCANNED the yard, an expansive tract of suburban real estate she
called the back 40. She was not pleased.
“Eliot, this is embarrassing. Our
property makes the Dust Bowl look
lush. Is it ever going to rain again?”
Her husband turned toward the sky
as if seeking an answer. But of course he
already knew what his response would
be, and so did she.
“October, Lily . . . It rains in October.”
“I don’t care about seasonal behav-
ior or what’s normal for the state. If it
doesn’t rain in Longmont or Loveland,
well, tough for them. I just care about
this backyard patch. Make it rain here,
will you? You’re the meteorologist.”
“Yes, dear, I am,” Eliot replied, flash-
ing a slight smile in the interests of do-
mestic tranquility, and went inside.
Despite the fact that Eliot had a
sheepskin testifying to his meteorological chops Lily’s gibe was a reminder he
wasn’t the man he had once hoped to be.
He wasn’t really a weather forecaster. In
high school, he could look at the clouds,
sense the temperature, and know a front
was moving in. Some of his friends admired his ability to predict afternoon
thunderstorms, while others thought he
was just obsessive. Eliot didn’t care much.
He enjoyed the fact that his skills could be
tested every day, his predictions verified
or disproved within hours. It was like
being a day trader, but without the risk.
Eliot followed his meteorological interests through grad school, but by the
late 2030s technology was rendering his
skills obsolete. Weather forecasts were
increasingly the province of computer
models, massive calculations that spit
out accurate predictions for any place
on the planet, down to an acre or less.
This development was inevitable, requiring only the ability to build a finer
grid of weather data—wind, temperature, barometric pressure—and the
compute power to crunch it all. Both
were now at hand.
Improved satellites had refined
the grid by a factor of 20 in all directions. The whole planet—continents,
ocean surface, the entire atmosphere
below six miles—was now sampled
on a scale of 300 feet. Every few minutes, the weather was measured and
binned into five trillion cells, the
mother of all spreadsheets.
It was Eliot’s job to feed this vast
anthill of numbers into the models several times a day and bring to bear the
compute power available in the Extended Cloud. Yes, he had to understand
what he was doing, and, yes, he had
to be careful. But it wasn’t traditional
weather forecasting, and definitely not
So Lily’s remarks bothered him.
Was he truly helpless? All his life
people had teased him with Charles
Dudley Warner’s bromide that everyone complains about the weather, but
no one does anything about it. But he
was a scientist. He knew that weather
involved immense highly energetic systems. The output of 100 power plants
was nothing compared to a hurricane’s
terawatts. How could anyone do anything about the weather? It was akin to
moving the Rocky Mountains.
He also knew the weather was a system that was chaotic and close to equilibrium. A small change could have big
consequences. The butterfly effect.
The idea that a butterfly could
precipitate a storm was a popular idea,
recognized centuries ago. Eliot had
thought about it in school but reckoned the flapping wings of a single
insect really couldn’t do much. And
But, like the weather, what can anyone do about it?
DOI: 10.1145/3140960 Seth Shostak
[CONTINUED ON P. 111]