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Severe Storm Forecasting
The Center for Analysis and Prediction of Storms & Pittsburgh Supercomputing Center
Norman, OK
USA

Year: 1997
Status: Award Recipient
Category: Science
Nominating Company: Silicon Graphics/Cray Research

Accurate four to six hour numerical forecasts of individual spring and winter storms help mitigate loss of property and lives, and reduce economic loss in agriculture, commercial transportation, and related areas.
I.Introduction

April 29, 1995. Around 10 p.m. a vicious thunderstorm rolls into
Dallas-Ft. Worth airport. Marble to softball-sized hail pelts planes on
the ground. More than 60 commercial jets must be removed from service
for repair. The direct insured loss exceeds $20 million, and further
loss from canceled flights over the next few weeks runs to $300 million.

What if there had been four hours warning? Incoming flights could have
been diverted, and with normal flight turnover, about two hours,
virtually all the planes would have been removed from harm's way,
cutting the airlines' loss and avoiding much of the subsequent
inconvenience to travelers. With countless other severe storms as well,
including the fierce tornadoes that rampage the Great Plains each
spring, better forecasts could greatly reduce property damage and save
lives. Since 1986, severe weather has hit the insurance industry with
unprecedented high wind-related losses, and industry studies indicate
that more than $14 billion a year of this loss could be avoided with
better forecasts.

Current forecasting gives about 30 minutes warning for storms, with
fairly imprecise information about extent and severity. The Center for
Analysis and Prediction of Storms (CAPS) at the University of Oklahoma
has developed a new storm-prediction capability that is setting
milestones in the field. In 1995, using the CRAY T3D at Pittsburgh
Supercomputing Center (PSC), their state-of-the-art computer model, the
Advanced Regional Prediction System (ARPS), successfully predicted the
location and meteorological structure of individual storms six hours in
advance, the first time anywhere this has been accomplished. In 1996,
ARPS did better yet, successfully forecasting the position and timing of
storms seven hours in advance, even though the storms hadn't yet begun
to form when the model was run.

II.Storm-Scale Weather Forecasting

The weather reports we watch on TV derive from computer models at the
National Centers for Environmental Prediction that predict atmospheric
structure over the continental United States as often as every three
hours. The local forecaster extracts from these models to show a
regional map, covering perhaps several states, with predictions that
weather the next day will be rainy, cloudy, sunny, etc.

In contrast, Kelvin Droegemeier and his colleagues at CAPS work on
storm-scale forecasting, a much tighter focus -- a few miles square in
space and about 15 minutes in time -- that corresponds to the scale on
which individual storms evolve. "What we're getting down to," says
Droegemeier, "is to say that over Pittsburgh this afternoon from 3:30 to
3:50 there will be a thunderstorm with winds of 30 miles per hour,
golfball-sized hail, two-and-a-half inches of rain, and by 3:50 it will
be gone. And to give you that forecast six hours in advance."

III.Progress

CAPS' goal is to develop storm-scale forecasting to the point where it
can be turned over to the National Weather Service early in the next
century, and their progress to date proves the feasibility of their
approach. Notable among the challenges they overcame since starting in
1988 is gathering the input data necessary to run a storm-scale model.
CAPS innovations now make it possible to gather all the data needed --
pressure, temperature, wind speed and more -- from Doppler radar.

Since 1993, CAPS has carried out experiments during Oklahoma's spring
storm season to see how ARPS works in an operational setting.
Initializing data each morning feeds the computer model running in
Pittsburgh. The output in turn feeds back to forecasters in Norman,
Okla. Early results were encouraging. In 1993, running on the CRAY C90
with limited data and a limited version of the model, forecasters used
ARPS information in an official National Weather Service forecast.

In spring 1995, CAPS used more ARPS capability, and for the first time
exploited parallelism on Pittsburgh Supercomputing Center's CRAY T3D.
This "highly parallel" computing system divides the computing among
hundreds of individual processors (up to 512) so that each processor can
work simultaneously on part of the job. Availability of this very
advanced computing system has been crucial to ARPS success.

"In meteorology," says Droegemeier, "getting results quickly is
essential. If you can't predict the weather significantly faster than it
evolves, the prediction has no value. If you're going to create a
four-to-six hour forecast, you better do it in half an hour. Parallel
computing and high-performance networking are crucial."

With the CRAY T3D harnessed to ARPS, the 1995 spring forecasting
experiment broke new ground in meteorology, successfully forecasting the
timing, location and general structure of a June 8 storm line that
extended from the northeast Texas panhandle across northwestern Oklahoma
up into Kansas. "The T3D's distributed, globally addressable memory,"
says Droegemeier, "gave us the ability to run at storm scale over a big
area at high resolution." The model did remarkably well, especially
considering that initializing data for these runs lacked the resolution
radar can provide. Nevertheless, the June 8 forecast showed accuracy
exceeding any prior storm modeling efforts. As a result of this success,
American Airlines contracted with CAPS to develop this embryonic
technology as a severe storm "smoke alarm" for airports.

In 1996, the spring operational test incorporated several model
enhancements, including more detail at ground level, that resulted in
better temperature forecasts and more precision in predicting the time
when storms would develop. With these improvements, ARPS successfully
forecast storms on eight of the 10 days when they occurred, an
unprecedented success rate. On several of these stormy days, data that
fed the model showed no convection, the earmark of storm conditions, and
the model still correctly forecast that storms would develop seven hours
later. Just as importantly, the model also correctly predicted no storm
on a day when conditions indicated a strong likelihood.

With this growing success record, CAPS researchers see their goals as
within reach, although challenges remain, perhaps none bigger than the
extreme computational challenge built into their work. In 1996, it took
about 100 minutes to run a seven-hour forecast using 256 processors of
the CRAY T3D. Fine for this stage of development, says Droegemeier, but
too long for real-world forecasting. The significantly improved
performance of newer computing technologies, such as the CRAY T3E
installed at PSC during 1996, will further boost ARPS ability to
forecast storm-scale phenomena.
Since 1986, insurance companies have paid unprecedented, and
unanticipated $60 billion in catastrophe losses. Nearly 90 percent of
these losses result from wind-related peril. The insurance industry
estimates that preventable loss from storms across many sectors of U.S.
industry -- agriculture, aviation, construction, communications, power,
manufacturing and transportation -- exceeds $14 billion a year.

Current forecasting gives about 30 minutes warning of impending
tornadoes or severe thunderstorms. With improved data and forecasting
technology, it will be possible to provide up to six hours warning, with
detailed information about the degree of severity and locations where
storms will strike. This will greatly reduce financial loss, and it will
save innumerable lives.
Every facet of CAPS is dependent on high-performance computing and
communications. In other areas of science and engineering, improved
performance allows one to view results more quickly, or perhaps in a
higher-quality form. In operational weather prediction, speed is the
bottom line: If the numerical model does not run significantly faster
than the weather evolves, producing a six-hour forecast in 15 minutes
for example, the model output has no practical use. Because of this,
ARPS requires the most advanced computing systems available. As
recognition of the computer-programming efficiency and design excellence
incorporated into ARPS, in 1993 it was a finalist for the prestigious
Gordon Bell Prize in high-performance computing.
The ARPS system employs highly innovative techniques for initializing
the computer model with Doppler radar data. A fundamental problem that
CAPS overcame since it started in 1988 was how to gather the
initializing data for the model. The singular difference in their
approach from other storm-modeling efforts has been to rely on Doppler
radar, which gives only the wind component parallel to the radar beam
and windfall intensity. From this information, CAPS has developed new
techniques -- based in control theory and mathematical minimization --
to infer the other necessary data: the full three-dimensional wind
field, pressure, temperature and water distribution. This is a major
technical accomplishment.

ARPS also makes use of a sophisticated nested-grid system for producing
high resolution forecast data for localized storm-scale weather. During
1996 operational tests, for instance, two six-hour forecasts were made
each day. The first used a 15 kilometer resolution horizontal grid for a
1,200 by 1,200 kilometer area centered over western Oklahoma. The second
forecast used three kilometer grid spacing for a 336 by 336 kilometer
area. This second fine-grid forecast area was determined from the daily
severe weather target area and nested within the large-grid area. This
method provides detailed forecast accuracy while efficiently exploiting
the available computing resources.

CAPS developed ARPS as a versatile program that can be run on all
classes of computers. ARPS users, which include several foreign
countries, have found that the user's guide itself represents an
innovation in software documentation. At the Pittsburgh Supercomputing
Center, ARPS demonstrated its versatility by first running on the CRAY
C90, a traditional parallel, vector supercomputing system. When the
scalable, parallel CRAY T3D system became available, CAPS adapted ARPS
to exploit the capability of this system to produce even faster results.
During the 1995 spring storm season in Oklahoma, with PSC's CRAY T3D
harnessed to ARPS, CAPS broke new ground in meteorology, successfully
forecasting the timing, location and general structure of a June 8 storm
line that extended from the northeast Texas panhandle across
northwestern Oklahoma up into Kansas. As a result of this success,
American Airlines negotiated a three-year contract with CAPS, investing
$1 million to test the new prediction technology as a "smoke alarm" for
airports.

In 1996, the spring operational test incorporated several model
enhancements, including more detail at ground level, that resulted in
better temperature forecasts and more precision in predicting the time
when storms would develop. With these improvements, ARPS successfully
forecast storms on eight of the 10 days when they occurred, an
unprecedented success rate. On several of these stormy days, data that
fed the model showed no convection, the earmark of storm conditions, and
the model still correctly forecast that storms would develop seven hours
later. Just as importantly, the model also correctly predicted no storm
on a day when conditions indicated a strong likelihood.

Although further improvements in computing will be necessary to realize
the full potential of ARPS, these results demonstrate that it can
successfully meet the goals of reliable storm forecasts four to six
hours in advance.
a.The first and most critical obstacle to overcome was, as mentioned
above, the retrieval of unobserved quantities from single-Doppler radar
data. Doppler radar measures only the radial (along-beam) component of
the wind field in the region where precipitation occurs. A numerical
prediction, however, needs the full three-dimensional wind field, as
well as other variables (e.g., pressure, temperature, humidity,
turbulence).

Because the fine-scale observations needed for storm prediction are
available only from Doppler radars (the new national NEXRAD network),
methods had to be developed to retrieve, from a time series of
observations from a single Doppler radar, the cross-beam and polar
(vertical) wind components and other variables. This posed an enormous
challenge.

CAPS has developed numerous techniques to overcome this difficulty,
ranging from simple methods to very complex and theoretically complete
approaches based on control theory. While theoreticians were concerned
about the uniquesness of the retrieved solution, experiments showed that
this was unlikely to be a problem. Rather, computational efficiency is
the key issue, as described above. b.The obstacle looming most
prominently in storm-scale prediction is the extent to which events in
the small-scale atmosphere can be predicted. It is now well-known that
the evolution of nonlinear dynamical systems can, in many cases, be
highly sensitive to the initial conditions (the Lorenz predictablity and
chaos problems). Given that thunderstorms and other storm-scale
phenomena are highly three-dimensional and last for tens of minutes or
an hour, it was clear that classical predictability theory (which
estimates the time needed for errors or uncertaintites in initial and
boundary conditions to grow to the extent that they render a forecast
indistinguishable from one chosen at random) did not apply.

So what on the small-scale is predictable, and for how long? Most
meteorologists felt that the small-scale structures present in events
like thunderstorms would preclude any element of practical
predictability. Through a series of yearly realtime operational tests of
its forecast system, however, CAPS has demonstrated that storm-resolving
models do indeed have "skill." Their forecasts can often pinpoint storm
location to within one or two counties some six to nine hours in
advance. Based on this success, as noted above (long summary), American
Airlines has invested a significant amount of money to transfer this
technology to is operations system in Fort Worth, Texas. All in all,
CAPS success represents an important breakthrough with incalculable
societal benefits.