Bolts from the blue

Weather forecasting
as we know it was born in the trenches of World War I, when a prophetic
young British meteorologist named Lewis Fry Richardson had a stroke
of genius. Richardson’s idea was to lay a grid over the landscape
and calculate the behavior of the atmosphere in each cell. The math
would be done by an array of 64,000 people gathered in a tremendous
amphitheater. Each person, armed with a mechanical calculator, would
compute the weather in a given cell based on physical equations,
observations streaming in from the field, and the results passed
along from neighboring cells. In one fell swoop, Richardson had
anticipated both modern forecasting and parallel computation.

Today, the computers
at the National Center for Environmental Prediction (NCEP) of the
National Oceanic and Atmospheric Administration (NOAA) and elsewhere,
fed by satellites and a global network of observatories, are a realization
in silicon of Richardson’s dream.

These computers
do a pretty good job of predicting the weather by laying a coarse
grid across the United States. The cells of the grid are currently
32 kilometers on a side, which makes it impossible to predict precisely
where a storm is likely to strike. They can say if it will rain,
but not if it will rain on your parade.

That was not
good enough for the organizers of the 1996 Summer Olympics in Atlanta.
They needed to know how clouds would affect equestrian events, if
dew would endanger racers at the Velodrome, if the wind would be
adequate for sailors or too strong for platform divers. And perhaps
most important, they needed to know if a thunderstorm would threaten
the elaborate closing ceremonies.

To satisfy the
needs of the Olympic organizers, a team of IBM researchers led by
Zaphiris Christidis worked closely with precise weather forecasts.
Christidis adapted a well-known mathematical model of the atmosphere,
the Regional Atmospheric Modeling Systems (RAMS), developed at Colorado
State University, to run on an IBM RS/6000 SP parallel computer.

For the Olympics,
the researches divided the Atlanta area on a grid that could resolve
weather events on a scale of between 1.5 and 5 miles and track their
evolution in 12-second increments. To make this model run fast enough
required careful tuning and optimization, an art that Christidis
excels at. "The target was to produce a 24-hour forecast,"
Christidis explains, "but the constraint was to produce that
forecast in just a few hours. After all, if you needed 24 hours
to do a 24-hour forecast, you might as well just open a window and
stick out your head." In the end, by optimizing the RAMS code
and choosing an SP that consisted of 28 processor nodes, Christidis
was able to generate a 24-hour forecast in less than three hours.

What brings
the project under the umbrella of Deep Computing is the act of combining
Christidis’s optimized weather code with Treinish’s visualization
tools to create a system that can be used to make real-world decisions.
"It’s putting the pieces together," says Treinish. "All
the pieces were already out there. We used a model that originated
at a university, visualization software developed here at Research
and available commercially, and the SP, which is also available
commercially. But you can’t just buy all these pieces and say, ‘Ok,
now we have an interactive forecaster’."

The most dramatic
test of the forecaster came on August 4, 1996, the day of the closing
ceremonies. The National Weather Service’s coarse-grained models
predicted that the Atlanta area would have thunderstorms, which
could have been disastrous if they struck the crowded stadium. Ordinarily,
the organizers would have delayed the ceremonies, which would have
meant an expensive anticlimax to the games. The IBM system produced
an animated 3D visualization that showed storm clouds sprouting
like mushrooms over the Atlanta area. Beneath the coluds were puddles
of blue corresponding to predicted rainfall, which swiftly moved
across the landscape, missing the stadium by 10 miles. The organizers
decided to proceed with the ceremonies. The storm followed the precise
track predicted by the IBM system, and the ceremonies went off without
a hitch.

The forecaster
has since been demonstrated around the United States. At a computer
show in San Jose, the IBM system-running on an SP cluster that was
also demonstrating Deep Blue-correctly predicted the pattern of
the next day’s rainfall. This feat so impressed reporters that the
local press dubbed the system Deep Thunder.

While Deep Thunder
is of obvious value to weather bureaus, it has sparked interest
in other quarters as well, including insurance companies, airlines,
utilities and agriculture. Visualization will be the key to such
varied applications. "You have to be able to disseminate the
forecast to a user who may not be a meteorologist," Treinish
says. "In some cases, you don’t even show the weather directly."

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