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
