Researcher Uses Multiple Methods To Predict Tornadoes Earlier
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Tornadoes are a common occurrence through the South and plains regions of the United States, causing millions of dollars in damage each year and claiming hundreds of lives. Meteorologists and researchers have long tried to gather enough data about these storms to create a sophisticated early warning system in order to predict these storms hours in advance and save lives.
Amy McGovern is a computer scientist at the University of Oklahoma who is working to find out more about how tornadoes form and why they form in some storms and not others.
“The problem is that if you need to understand the atmosphere, there are a whole lot of variables out there,” McGovern said in an NSF report. “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.
Video from storm chasers and Doppler radar can help meteorologists understand some basic aspects of tornadoes. McGovern, however, uses supercomputers and data mining to gather important information about tornado formation.
“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.
In addition to studying real storms, McGovern and her team use supercomputers to create simulation storms to analyze how constantly changing storm components interact with one another. The simulations created by these supercomputers can often create more than 1 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.
Not all of the information created by these simulations is completely accurate, however, and no hardware exists to reliably capture all the data needed to predict tornadoes from within the storm.
This doesn´t make the prospect of melding together computer science and meteorology any less exciting to researchers.
Kevin Droegemeier is the professor of meteorology and vice president of research at the University of Oklahoma and has studied storms in the area known as “Tornado Alley” for years.
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,” Droegemeier said
“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.
While the ability to predict tornadoes earlier would be a breakthrough advance, there is another issue to be concerned with; the issue of 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.
McGovern hopes to make a larger point with her models, however. Her aim is to prove to her students the importance of computer science and how it can be used to make a difference in the real world.