Back in July 1990, Boulder, Colo., and surrounding communities were hit by one of the most freakish weather disasters on record. A severe summer storm pelted the area with hailstones, balls of ice that in some cases, were big as baseballs.
According to a local TV station's account, thousands of homes sustained major damage to roofs and windows, and some automobiles were so badly battered that they were a total loss. The frozen barrage stripped paint from street signs and ripped foliage off trees. At an amusement park, 47 people were injured when they were caught on a roller coaster during the sudden storm. All in all, the area suffered about $1 billion in losses from the hail.
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Monster hail storms like that don't happen very often, fortunately. But when they do, it can be pretty scary. Unfortunately, while scientists know basically that storms with a rotating updraft on their southwestern sides tend to produce both the biggest, most severe tornadoes and a lot of large hail, there's still a lot that they don't know about how such storms form and how to predict the worst ones in advance.
That's why a team of University of Oklahoma researchers, with support from the National Science Foundation grant, working on using a supercomputer to develop a better understanding of severe hail storms.
The ominously-named Severe Hail Analysis, Representation and Prediction project, AKA SHARP, utilizes the Stampede supercomputer at the Texas Advanced Computing Center at the University of Texas at Austin to perform experimental weather forecasts.
To predict hailstorms and other weather, scientists have used math and physics to develop models that represent the storms on a grid consisting of millions of points. The finer the resolution of the grid, the more accurate the forecast. The SHARP team is using a grid point for every 500 meters - six times the resolution of the models currently used by the National Weather Service.
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That, in turn, allows the scientists to predict the behavior of storms more accurately. In a simulation of a massive tornado that hit Oklahoma in 2013, they produced mock forecasts that described the storm's impact better than actual NWS forecasts from that time.
"This has the potential to change the way people look at severe weather predictions," explained research scientist Nathan Snook in a press release. "Five or 10 years down the road, when we have a system that can tell you that there's a severe hail storm coming hours in advance, and to be able to trust that - it will change how we see severe weather. Instead of running for shelter, you'll know there's a storm coming and can schedule your afternoon."