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12 Whirling Facts About Tornadoes

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Often spinning at speeds over 100 miles per hour (and in extreme cases over 300 miles per hour), a tornado is a violently rotating column of air in contact with the Earth and the clouds that can cause considerable destruction. The very large and very powerful Tuscaloosa-Birmingham tornado of 2011 lofted a 36-ton empty coal hopper rail car almost 400 feet. The equally impressive Hackleburg tornado of the same day carried jeans from a damaged denim factory more than 40 miles. Here are 12 facts about these dangerous whirlwinds.


When winds higher in the atmosphere are moving faster than wind closer to the ground, this creates vertical wind shear, which is a change in wind speed or wind direction with height. Much like a paddle wheel, this wind shear generates horizontal rotation. But to become a tornado, this horizontal rotation needs to become vertical. When a cool, dry air mass covers warm moist air, the overlap creates instability. The hot air wants to rise because it’s less dense, forming updrafts. This updraft can tilt the horizontal rotation into vertical rotation—the beginnings of a tornado.

A cap of warmer air can prevent this rotation from tilting, because it can block the updrafts from penetrating very high into the atmosphere. But if conditions change—say, as the heat of the day reaches its peak by mid- to late-afternoon—rising air from the surface layer of air becomes warmer than the cap, breaking it. Air can now ascend several miles into the sky. A thunderstorm with a rotating updraft—a supercell—has now developed.

However, even when all these ingredients are present, the supercell may not produce a tornado. Scientists are still trying to understand exactly what the triggering mechanism is that turns a supercell into a twister. “The atmosphere has a way of getting the four together in ways with minor differences to either create a large EF5 tornado or a just some rain. We don’t know when and where these ingredients form in just the right way,” Roger Edwards, lead forecaster at the Storm Prediction Center, told Science of the South. Indeed, 70 percent of tornado warnings issued are for storms that never produce tornadoes. It may seem like crying wolf, but think of the 30 percent of warnings that are accurate. And not all tornadoes come from supercells: With names like gustnado and landspout (cousin to the more famous waterspout), these form in unique ways but are considerably weaker than supercell tornadoes.


All tornadoes in the U.S. from 1950–2013 based on data from the NOAA Storm Prediction Center. Image credit: Wikipedia Commons // CC BY-SA 4.0

Tornadoes have occurred on every continent except Antarctica. However, the region known as Tornado Alley, in the south-central U.S., has earned that name for a good reason: Though it accounts for just 15 percent of the land in the U.S., it's seen nearly 30 percent of the country's tornadoes, with 16,674 twisters touching down here between 1950 and 2010. It averages 268 tornadoes per year. These tornadoes arise because of a clash between warm moist air from the Gulf of Mexico near the ground, colder air in the upper atmosphere from the west, and a third layer of very warm dry air between the two levels from the southwest that tries to keep the other two at bay.


Researchers at the University of Alabama at Huntsville have discovered that topography and roughness of the landscape can also influence the power of a tornado. In simulations, the "rougher" the area is, the stronger and wider a tornado can get. Forested areas have a rougher surface than open agricultural areas, and forested mountains are even rougher, according to Kevin Knupp, lead of the Alabama research team. But the picture is more complicated than that, according to his colleague Anthony Lyza, who has found that tornadoes in Alabama are affected by topography. According to Lyza, tornadoes weaken as they proceed up mountains and hills—but they strengthen as they proceed down. And sometimes, regardless of whether a tornado is moving up or down a hill or mountain, the land mass will cause a tornado to dissipate.


Tetsuya Fujita was a Japanese meteorologist recruited in 1953 to the University of Chicago. The town he lived in at the end of World War II was the primary target of one of the atomic bombs the U.S. dropped. Due to cloudy conditions, that bomb was dropped on its secondary target—Nagasaki. Fujita’s study of the damage of the nuclear bomb blasts actually led to the discovery of meteorological phenomena called microbursts.


Before 1971, all tornadoes were essentially treated the same, regardless of strength, size, path, or damage zone. That year, Fujita released his method of categorizing them: The F-scale, which measures the wind speed of a tornado—indirectly. Because of difficulties getting accurate wind speeds inside a tornado, Fujita looked at how much destruction various tornadoes caused and back-calculated wind speeds based on that. He then created a scale that ranged from F1 to F12, linking together the Beaufort scale of wind strength, long used by mariners and meteorologists, and Mach scale (yes, like jets). An F1 tornado corresponds to a 12 on the Beaufort Scale, and an F12 corresponds to Mach 1. He then added an F0 (40-72 mph) to have a baseline at a level that wouldn’t cause appreciable damage to most structures (influenced by Beaufort’s 0 - calm/no wind), and maxed the tornado part of the scale at F5 (261-318 mph). An F5 is the highest rating given to a tornado, because Fujita believed this to be the theoretical upper limit for how fast winds in a tornado could reach.

An F0 causes light damage to chimneys, breaks tree branches, and damages billboards. An F5 causes incredible damage. It can lift framed houses off their foundations and carry them a considerable distance. It can toss cars more than 300 feet through the air. It can completely debark trees. Even steel-reinforced concrete isn’t safe.


According to meteorologist Charles A. Doswell, there are problems with using the F-scale. “The real-world application of the F-scale has always been in terms of damage, not wind speed," he told Science of the South. "Unfortunately, the relationship between the wind speeds and the damage categories has not been tested in any comprehensive way.”

In 2004 and 2005, dozens of meteorologist and civil engineers collaborated through a research center at Texas Tech University on a more objective scale, which they named the Enhanced Fujita Scale. A year later, the EF-scale went into use in the U.S. The EF-scale has more rigorous and standardized measures of damage; adds additional building and vegetation types; accounts for differences in construction quality; dramatically lowers the wind speeds associated with stronger tornadoes; and expands degrees of damage. Or, as the tornado-chasing character played by Bill Paxton in Twister puts it, “It measures a tornado’s intensity by how much it eats.”


Although radar originated in the 1930s, it wasn't used for the weather until the 1950s. The first radar detection of a tornado occurred in 1953, using a radar designed for naval aircraft. Far more important was the discovery of the tornado vortex signature in 1973, based on observation of a tornado in Union City, Oklahoma. What scientists discovered was that there was a telltale pattern that appeared before the tornado formed.

Before then, researchers had used films, photos, or damage markings for clues. The discovery of the tornado vortex signature led to the modern tornado warning system in the U.S., including a national network of next-generation Doppler radars (NEXRAD, also known as WSR-88D) funded by Congress.


The tornado vortex signature appears on the radar as red/yellow (indicating high outbound velocity) and green/blue (inbound velocity) pixels occurring adjacent to each other over a relatively small area. This is also called a velocity couplet, and it’s associated with the mesocyclone, the rotating vortex of air within the supercell. Radar can also be used to detect a hook echo extending from the rear part of the storm, resulting from precipitation wrapping around the backside of the rotating updraft. Terrifyingly, radar can also detect the debris ball from a tornado; objects lofted into the air by a tornado reflect radar waves very well.


The tornado season of 2011, known as the Super Outbreak, was one of the most deadly in U.S. history, with 59 tornadoes in 14 states causing 552 fatalities. Most of these deaths occurred in Alabama and Missouri. The three most deadly tornadoes of 2011 were the Joplin, Missouri EF5, which took 159 lives; the Western Alabama EF5, which claimed 72; and the Tuscaloosa-Birmingham EF4, which killed 64. Six of the top 10 deadliest tornadoes that year occurred in Alabama. April 27, 2011, was the deadliest tornado day in the U.S. since March 18, 1925. 


From 1985 to 2010, more tornado-related deaths in the Southeast U.S. occurred in mobile homes than any other structure. In the decade before 2011, one-half of all fatalities occurred in mobile homes. Some of this is related to the fact that the Southeast in general has more mobile homes.


A year after the 2011 Super Outbreak, a team of scientists assessed 2000 adolescent survivors of the tornadoes for signs of major depressive episodes (MDE) and post-traumatic stress disorder (PTSD). Roughly 1 in 15 adolescents suffered from PTSD and 1 in 13 developed MDE. Unsurprisingly, both also occurred in greater frequency when a family member had been injured. Nearly one-third of the children surveyed suffered from hyperarousal—a state of tension produced by hormones released during the fight-or-flight reaction—and re-experiencing (or reliving) the event.


Despite the continued occurrence of massive tornadoes, fatalities from these weather phenomena continue to decline. Until the 1930s, the average death toll from tornadoes was well above 200 per year. Since the late 1990s, that average now hovers near 50 deaths per year. Thanks to better technology, models, and data, scientists can increasingly predict—and warn of—conditions that are likely to produce a tornado.

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iStock // Ekaterina Minaeva
Man Buys Two Metric Tons of LEGO Bricks; Sorts Them Via Machine Learning
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iStock // Ekaterina Minaeva

Jacques Mattheij made a small, but awesome, mistake. He went on eBay one evening and bid on a bunch of bulk LEGO brick auctions, then went to sleep. Upon waking, he discovered that he was the high bidder on many, and was now the proud owner of two tons of LEGO bricks. (This is about 4400 pounds.) He wrote, "[L]esson 1: if you win almost all bids you are bidding too high."

Mattheij had noticed that bulk, unsorted bricks sell for something like €10/kilogram, whereas sets are roughly €40/kg and rare parts go for up to €100/kg. Much of the value of the bricks is in their sorting. If he could reduce the entropy of these bins of unsorted bricks, he could make a tidy profit. While many people do this work by hand, the problem is enormous—just the kind of challenge for a computer. Mattheij writes:

There are 38000+ shapes and there are 100+ possible shades of color (you can roughly tell how old someone is by asking them what lego colors they remember from their youth).

In the following months, Mattheij built a proof-of-concept sorting system using, of course, LEGO. He broke the problem down into a series of sub-problems (including "feeding LEGO reliably from a hopper is surprisingly hard," one of those facts of nature that will stymie even the best system design). After tinkering with the prototype at length, he expanded the system to a surprisingly complex system of conveyer belts (powered by a home treadmill), various pieces of cabinetry, and "copious quantities of crazy glue."

Here's a video showing the current system running at low speed:

The key part of the system was running the bricks past a camera paired with a computer running a neural net-based image classifier. That allows the computer (when sufficiently trained on brick images) to recognize bricks and thus categorize them by color, shape, or other parameters. Remember that as bricks pass by, they can be in any orientation, can be dirty, can even be stuck to other pieces. So having a flexible software system is key to recognizing—in a fraction of a second—what a given brick is, in order to sort it out. When a match is found, a jet of compressed air pops the piece off the conveyer belt and into a waiting bin.

After much experimentation, Mattheij rewrote the software (several times in fact) to accomplish a variety of basic tasks. At its core, the system takes images from a webcam and feeds them to a neural network to do the classification. Of course, the neural net needs to be "trained" by showing it lots of images, and telling it what those images represent. Mattheij's breakthrough was allowing the machine to effectively train itself, with guidance: Running pieces through allows the system to take its own photos, make a guess, and build on that guess. As long as Mattheij corrects the incorrect guesses, he ends up with a decent (and self-reinforcing) corpus of training data. As the machine continues running, it can rack up more training, allowing it to recognize a broad variety of pieces on the fly.

Here's another video, focusing on how the pieces move on conveyer belts (running at slow speed so puny humans can follow). You can also see the air jets in action:

In an email interview, Mattheij told Mental Floss that the system currently sorts LEGO bricks into more than 50 categories. It can also be run in a color-sorting mode to bin the parts across 12 color groups. (Thus at present you'd likely do a two-pass sort on the bricks: once for shape, then a separate pass for color.) He continues to refine the system, with a focus on making its recognition abilities faster. At some point down the line, he plans to make the software portion open source. You're on your own as far as building conveyer belts, bins, and so forth.

Check out Mattheij's writeup in two parts for more information. It starts with an overview of the story, followed up with a deep dive on the software. He's also tweeting about the project (among other things). And if you look around a bit, you'll find bulk LEGO brick auctions online—it's definitely a thing!

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Smart Shopping
This Week's Best Amazon Deals You Can Still Get
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As a recurring feature, we share some amazing Amazon deals we’ve turned up. These items were the ones that were the most popular with our readers this week, and they’re still available.

Mental Floss has affiliate relationships with certain retailers, including Amazon, and may receive a small percentage of any sale. But we only get commission on items you buy and don’t return, so we’re only happy if you’re happy. Good luck deal hunting!


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