Computing Community Consortium Blog

The goal of the Computing Community Consortium (CCC) is to catalyze the computing research community to debate longer range, more audacious research challenges; to build consensus around research visions; to evolve the most promising visions toward clearly defined initiatives; and to work with the funding organizations to move challenges and visions toward funding initiatives. The purpose of this blog is to provide a more immediate, online mechanism for dissemination of visioning concepts and community discussion/debate about them.

Improving Our Ability to Predict Tornadoes

March 26th, 2012 / in big science, research horizons, Research News / by Erwin Gianchandani

Today’s National Science Foundation (NSF) Science Nation features the work of Amy McGovern, an Associate Professor of Computer Science and Adjunct Associate Professor of Meteorology at the University of Oklahoma, whose data mining and predictive modeling approaches are transforming the way we predict tornadoes.

According to the article:

Amy McGovern, University of Oklahoma [image courtesy NSF/Science Nation].Tornadoes claim hundreds of lives and cause billions of dollars in damages in the United States. But the tornado outbreak across the South on April 27, 2011, was startling, even for veteran forecasters such as Greg Carbin at the National Oceanic and Atmospheric Administration (NOAA) Storm Prediction Center (SPC) in Norman, Okla.


“Through the 24-hour loop here, almost 200 tornadoes had occurred in that period of time and, unfortunately, over 315 fatalities. Primarily Alabama was hit hardest but also fatalities in Tennessee, Georgia, Mississippi, and Virginia for this event,” says Carbin.


As the warning coordination meteorologist at SPC, he would like to see tools that could help predict these killer storms.


“So, that was sobering, you know. Why? Why so many fatalities? Why so much destruction?” asks Carbin [more, including video, after the jump].


With support from the [NSF], computer scientist Amy McGovern at the University of Oklahoma is working to find answers to key questions about tornado formation. Why do tornadoes occur in some storms, but not in others?


“The problem is that if you need to understand the atmosphere, there are a whole lot of variables out there,” says McGovern. “There’s pressure, there’s temperature, there’s the wind vector. And none of the radars, none of the current sensing instruments can get that at the resolution that we really need to fundamentally understand the tornadoes,” she says.


Amy McGovern, University of Oklahoma, displaying some of her work [image courtesy NSF/Science Nation].While video from storm chasers and data from Doppler radar can help meteorologists understand some aspects of tornadoes, McGovern uses different, powerful tools: supercomputers, and a technology known as data mining.


“Data mining is finding patterns in very large datasets. Humans do really, really well at finding patterns in small datasets but fail miserably when the datasets get as large as we’re talking about,” she says.


McGovern and her team don’t just study “real” storms. They create supercomputer simulations to analyze how constantly changing storm components interact. And each storm they create may generate a terabyte of data.


“What we’re doing with our simulations is actually being able to sense all of these fundamental variables every 50 to 75 meters,” she explains.


She works with many weather experts, including research meteorologist Rodger Brown, at the National Severe Storms Laboratory. He’s helping to sort out what’s usable information in the simulation, and what’s not needed within the enormous amount of data. He points out some of the “unknowns” on a computer animation.


“There’s some of the bad noise bouncing off the edge of the grid. Somehow, it’s noise being amplified, some sort of gravity waves or something. So we’re going to have to do some more experimentation to find out what the problem is,” says Brown.


Even though there are still many questions to answer with these simulations, McGovern says there’s no hardware right now that could be deployed in a tornado to get so many observations. And even if it did exist, it would likely get destroyed in just about any storm.


Kelvin Droegemeier, professor of meteorology and vice president of research at the University of Oklahoma, has studied severe weather in “Tornado Alley” for many years. He’s excited by the collaboration of meteorology and computer science.


“It’s a game-changer, complete game-changer. Radar leads off basically with detecting something that’s already present; the numerical model gives us the opportunity to actually project it and predict it far in advance,” he says.


“So, instead of warning on a detection based on radar [and] some visual sighting, you’re actually warning based on what a numerical forecast model will tell you. So, imagine a tornado warning being issued before a storm is even present in the sky,” says Droegemeier…


But while the ability to predict tornados so far in advance would be a breakthrough, there’s another variable that is even harder to predict than a dangerous storm: human behavior.


“That’s a very interesting challenge that also brings in the whole social behavioral issues of, how would people react. Would they kind of dismiss that as, well, there’s not even a storm, I looked outside, the skies are clear?” says Droegemeier…

Read much more about McGovern’s work in the Science Nation story — and check out a companion video below.

(Contributed by Erwin Gianchandani, CCC Director)

Improving Our Ability to Predict Tornadoes

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