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Supercomputing Tornadogenesis

Over at TACC, Faith Singer-Villalobos writes that XSEDE supercomputer resources are helping researchers understand tornados with scientific visualization and data mining.

Negative gradient threshold (blue) depicts primary (big part) and low-level (lower tube part) updrafts, Strong vorticity (red) is wrapping into the primary updraft. The scientists hypothesize the evolution of this gradient differs between tornadic and tornado failure storms.

Amy McGovern, a computer scientist at the University of Oklahoma, has been studying tornadoes, nature’s most violent storms for eight years. She uses computational thinking to help understand and solve these scientific problems. Computational thinking is a way of solving problems, designing systems, and understanding human behavior that draws on concepts fundamental to computer science. In science and engineering, computational thinking is an essential part of the way people think about and understand the world.

“Since 2008, we’ve been trying to understand the formation of tornadoes, what causes tornadoes, and why some storms generate tornadoes and other storms don’t,” McGovern said. “Weather is a real world application where we can really make a difference to people.” She wants to find solutions that are useful.

Tornadogenesis is the process by which a tornado forms. There are many types of tornadoes, and each type of tornado can have several different methods of formation. Specifically, she is trying to identify precursors of tornadoes in supercell simulations by generating high resolution simulations of these thunderstorms. Supercell storms, sometimes referred to as rotating thunderstorms, are a special kind of single cell thunderstorm that can persist for many hours. They are responsible for nearly all of the significant tornadoes produced in the U.S. and for most of the hailstorms larger than golf ball size. McGovern would like to generate as many as 100 different supercell simulations during this project.
In addition to high resolution simulations, McGovern is also using a combination of data mining and visualization techniques as she explores the factors that separate tornado formation from tornado failure.

Studying tornadoes and violent weather comes with a high learning curve, as it requires the application of science and technology to predict the state of the atmosphere for a given location. When McGovern first started the research with a National Science Foundation (NSF) Career Grant, she had to attend several classes so that she would understand more about meteorology, the interdisciplinary scientific study of the atmosphere. She worked closely with meteorology students who taught her about the atmosphere, and she, in turn, taught them about computer science. They went back and forth until they understood each other.

The early research generated by the NSF Career Grant resulted in developing data mining software and developing initial techniques on lower resolution simulations.

Now, we’re trying to make high resolution simulations of super cell storms, or tornadoes,” McGovern said. “What we get with the simulations are the fundamental variables of whatever our resolution is — we’ve been doing 100 meter x 100 meter cubes — there’s no way you can get that kind of data without doing simulations. We’re getting the fundamental variables like pressure, temperature and wind, and we’re doing that for a lot of storms, some of which will generate tornadoes and some that won’t. The idea is to do data mining and visualization to figure out what the difference is between the two.”

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