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The United States just experienced the busiest stretch of tornado activity in more than a decade. 

Between April 25 and May 27, there were only two days when tornadoes didn’t touch down. According to a preliminary tally from the National Centers for Environmental Information, 1,117 tornadoes were detected between January and May of this year, the highest count over this time frame since 2011. 

These menacing funnels of spinning air are deadly. Twisters over Memorial Day weekend killed at least 21 people across states including Kentucky, Arkansas, Oklahoma, and Texas. They’ve racked up billions of dollars in damages. They’ve also dropped down from the sky in places that rarely see them, like central California and outside of Washington, DC, forcing people who may have never experienced these storms before to seek shelter that may not exist.

Tornadoes remain one of the most dangerous weather events. And they buck an otherwise promising trend: While many types of natural disasters are killing fewer people over time thanks to better forecasting and stronger infrastructure, tornadoes can catch people off guard. 

The lead time for tornado warnings is often less than 10 minutes, and progress has been frustratingly slow, especially when compared to other types of severe weather. 

In the last few years, scientists have made progress in anticipating when the next twisters will touch down. In particular, forecasters are now testing a new set of tools built on machine learning, an artificial intelligence technique that trains computers to detect patterns without explicitly programming them. 

These algorithms depend on good data to teach them, and that poses a major challenge for getting ahead of this particularly confounding phenomenon: As global average temperatures rise and as land use changes, past tornado activity might not reflect how these storms will whip through cities in the future. 

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One of the biggest obstacles to forecasting tornadoes is their size. “In the grand scheme of the atmosphere, they're very small-scale,” said Russ Schumacher, a professor of atmospheric science at Colorado State University. “The biggest ones might be a mile wide. Most of them are smaller than that.” Tornadoes can rip entire homes off their foundations while houses a few blocks away are left unscathed. 

Tornadoes are also short-lived, often just a few minutes. Detecting tornadoes with instruments like Doppler radars requires looking for subtle cues and still needs verification from storm spotters on the ground. Weather monitoring stations are often spaced too far apart to pick up smaller tornadoes before they form. 

Hurricanes, in contrast, gather strength over days, can span hundreds of miles, and are visible to satellites, yielding ample time and information to generate useful forecasts, issue alerts, and get people out of the way. “I don’t think we’re ever going to have the level of specificity of forecasts for tornadoes that we do for hurricanes,” Schumacher said. 

Most tornadoes erupt from a particular type of thunderstorm known as a supercell, which contains a rotating column of air that moves upward. But not every supercell leads to tornadoes, and not every tornado hatches from a supercell. 

“Forecasters now are really good at identifying the days when the ingredients are in place, when the potential is there for a lot of tornadoes to happen,” Schumacher said. “But it’s still really difficult to identify which of those storms is going to make a tornado.”

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While it’s been difficult, there have been improvements in tornado forecasting over the past decade, and artificial intelligence has sped up progress more recently. 

As AI could boost potential here,  researchers had already refined numerical models and enhanced their resolution in the past decade, creating a sharper picture of how severe weather forms, particularly the kinds of storms that allow the convection needed to create supercells. 

Scientists have also developed a better understanding of how tornadoes are influenced by broader global factors. The recent burst of tornado activity was influenced by the shift away from the Pacific Ocean’s warm phase of its temperature cycle, known as El Niño.

Since the Pacific Ocean begins to telegraph when it’s likely to shift gears months in advance, this swing between El Niño and La Niña can be a warning sign that more tornadoes are brewing. The intense heat wave over Central America and Mexico last month then evaporated plenty of water into the atmosphere that served as fuel for convective storms. 

Now scientists are taking these historical records, present weather measurments, and computer simulations and feeding them into machine learning models to better predict tornadoes. One such forecasting model that’s currently undergoing testing at the National Weather Service’s Storm Prediction Center could anticipate heightened tornado activity over a region several days in advance of a strike. 

Schumacher said the machine learning system has proven especially useful roughly three to seven days ahead of a storm — a period when forecasters don’t have a lot of other tools that can make useful predictions in that time frame.

“I think the human forecasters tend to be a bit conservative,” Schumacher said. “[The machine learning tool] tends to be a little bit more bullish even at those longer lead times, but it’s turned out that a lot of the time it’s right.”

But scientists don’t want to take their hands off the radars and leave everything up to the AI just yet either. Victor Gensini, a meteorology professor at Northern Illinois University who studies tornadoes, dubbed the current strategy “human-in-the-loop AI,” where a meteorologist evaluates predictions from the machine learning model to ensure they line up with the laws of physics.

At the same time, researchers also want to keep an open mind and an eye out for any new, previously unrecognized relationships in weather that can cause tornadoes that might show up in the AI forecast. 

“As an expert, you look at some of these and you’re like, ‘That doesn’t make any sense. Why is the model weighting that?” Gensini said. “Maybe it’s picking up on something.”  

The big challenge for machine-learning forecasts, however, is that they’re learning from history. 

Robust tornado records don’t go back that far and there are lots of gaps in sensor networks. And as humans alter the flows of rivers, cut down forests, and change the climate, future tornadoes will arise in a regime that looks less like the past. “If you’re seeing something or trying to forecast something that's never happened before, then the model gets into some trouble,” Gensini said. 

That’s why a key part of developing better tornado forecasts is gaining better observations. To catch the tornado of the future, we need more eyes on the weather of the present.

 —Umair Irfan, correspondent

Ugur Yildirim/ dia images via Getty Images

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