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June 2004

Identifying storms that produce tornadoes

One of the perennial challenges in severe-weather forecasting is picking out which thunderstorms might produce a tornado. Most significant tornadoes emerge from mesocyclones, areas of rotating wind a few miles wide within a supercell thunderstorm. However, only a small fraction of mesocyclones results in twisters. Even some of the largest, most intense mesocyclones—such as the one that produced a record hailstone in Nebraska last June—may be unable to spin up a tornado.

Huaqing Cai

Huaqing Cai. (Photo by Carlye Calvin.)

An ASP postdoctoral researcher, Huaqing Cai, is working on a technique that could help forecasters identify the cells most likely to produce tornadoes amid a batch of severe thunderstorms. Huaqing, now in the second year of his fellowship, has been collaborating with Wen-Chau Lee (ATD) and Roger Wakimoto (University of California, Los Angeles).

To get a new perspective on the problem, Huaqing turned to fractals. Introduced by Benoit Mandelbrot in the 1970s, fractal geometry is a mathematical representation of patterns in nature. For example, while a mile-long ruler would give one measurement of a coastline, it would miss smaller curves. Fractal theory emphasizes how rulers of different lengths give a variety of measurements that sometimes add up to a more complete picture.

Huaqing applied this technique to radar data of five mesocyclones, three of which spawned tornadoes. Much like using different-sized rulers, he analyzed the data with a variety of horizontal grid scales. The resolution varied from 0.3 to 9.6 kilometers (0.19 to 6 miles).

For each storm, he examined how the maximum vorticity (a property of air flow rotating around an axis) changed with different scales.

As fractal theory would predict, vorticity and scale are tightly correlated. Maximum vorticity declines with larger grid spacing because the larger grids fail to capture intense, fine-scale vortices (just as a larger ruler might fail to capture a sharp indentation in a coastline).

But Huaqing found that the slope of that relationship varies in an interesting way: it’s apparently steeper for tornadic than for nontornadic mesocyclones. The more sharply the mesocyclone’s maximum vorticity declines as the grid spacing increases (which is an indication of more energy fueling small-scale, rather than large-scale, vortices), the greater the likelihood of a tornado.

tornado chart

This chart shows an analysis of maximum vorticity (vertical axis) versus horizontal grid spacing (horizontal axis) on a logarithmic scale for five supercell thunderstorms with mesocyclones. The steepest gradients are for the Kellerville and Garden City meso­cyclones, both of which spun up tornadoes (rated F3 and F1, respectively). The gradients for the San Angelo (very weak tornadic, not rated), Hays (nontornadic), and Superior (nontornadic) are more gradual. (Courtesy Huaqing Cai.)

If this relationship is confirmed by more data, it could eventually lead to on-the-fly radar analysis to distinguish the most tornado-prone storms, if the storms are not too far away from radar.

Huaqing says that more work needs to be done to understand the physical basis for the relationship he’s discovered. “This isn’t a tornadogenesis theory. It doesn’t tell you why a mesocyclone produces a tornado; it just tells you which one might.” •Bob Henson


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