The race to get ahead of the next tornado

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.

The complex physics powering these whirling towers of wind requires the processing power of supercomputers to simulate. Once they form, tornadoes can touch down, lift up, and change direction with little notice. That makes it hard to issue tornado warnings more than a few minutes in advance.

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. According to Schumacher, they need four basic ingredients to form: a lifting mechanism that pushes air upward, instability in the atmosphere that allows that air to climb higher, a large quantity of moisture to fuel the thunderstorm, and wind shear that changes direction with altitude, thus causing the storm to rotate.

But not every supercell leads to tornadoes, and not every tornado hatches from a supercell. The specific strengths and quantities of the ingredients have to be just right. A little more wind here, or a bit more moisture there, can make the difference between an ordinary thunderstorm and a rampaging swarm of twisters.

“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.”

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. Scientists have already developed AI weather forecasting systems that can outperform conventional techniques in some respects, but tornadoes remain a challenging test case. “That has the potential to make big advances but it's still kind of in its very early stages in terms of evaluation,” Schumacher said. “This part of the field has evolved just in the last two years, so it’ll be really interesting to see in two or five years from now where it is.”

One of the conventional ways to predict weather is using numerical models, where scientists plug their observations into complicated physics equations that generate a prediction of how weather will play out. They require good measurements, a robust understanding of the mechanisms at work, and a lot of time-consuming computational horsepower.

Researchers refined these 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. Right now, the world is coming out of one of the strongest El Niños on record, and the Pacific Ocean is shifting into La Niña, its cool phase. As this shift happens, water temperature in the equatorial Pacific tends to introduce disruptions in the atmosphere above the continental US, creating a fertile breeding ground for tornadoes.

“When El Niño decays, the atmospheric waves change and can become wavier, so they have a greater amplitude,” wrote meteorology researcher Jana Lesak Houser in The Conversation. “The US often sees more frequent tornadoes when the climate is transitioning out of 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. Similarly, changes in the Indian Ocean’s temperature cycles can create ripples that lead to more spinning storms over North America. Known as the Madden-Julian Oscillation (MJO), these cycles create atmospheric disturbances over shorter time scales that move eastward across the world and over the continental US.

“El Niño sets the stage and then the MJO is the conductor of the orchestra,” explained Victor Gensini, a meteorology professor at Northern Illinois University who studies tornadoes. “We had several MJO cycles this year.” 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.

The idea is to use past predictions from numerical models and line them up with historical observations of tornadoes. The machine learning algorithm then connects the dots between the meteorological starting conditions and where severe weather later emerges.

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.