Technology similar to what Facebook uses for recommending what friends you should “tag” could be coming to hailstorms. David Gagne, a scientist at the National Center for Atmospheric Research, is using facial recognition technology to unlock the secrets behind big hail.
“I’m using artificial intelligence techniques to predict the size of hailstorms,” he said. Working with computer-simulated storms, he created software that is trained to determine which storms produce hail and then to recognize patterns associated with the storms behind the largest hailstones. “The shape of storms is really important.”
His latest work is published in Monthly Weather Review.
Gagne’s novel approach started with his Ph.D. dissertation between 2014 and 2015. It continued with a postdoc fellowship at NCAR, where he used “deep learning” to look at storms and find spatial patterns in storm data. Past studies often looked at finer-scale processes within the storm. Gagne is taking the opposite approach, broadening to consider the storm’s entire structure.
The work he’s doing deals with computer-generated storms. “We create storms and derive their hail size with the microphysics,” he said. Gagne then uses the raw data of what the storm “looks” like structurally to train software to predict its hail size. Gagne’s machine learning model is improving its predictions with each run.
Why not deal with actual hailstorms? “Simulated storms are a more self-consistent system,” he said. In real life, there are many more complicating variables that render an experimental data set incomplete.
“The data we have is skewed,” Gagne said. “The hail reports cluster near cities or interstates.” In rural areas, the largest hail may strike in areas where nobody lives, leading to a missed event. Public-submitted hail reports may not be mapped correctly. Doppler radar data could be used to fill in the gaps, but that comes down to radar coverage — which is somewhat lacking in many hail-prone areas. “And Doppler-estimated hail size has its own biases,” he said.
Philippe Tissot, a researcher at Texas A&M, said Gagne is “leading the field.”
Paul Miller, a professor at Louisiana State University, said he thinks machine learning can help forecasters sort out some of the randomness in an atmospheric setup.
“Even on days that we believe favorable for severe thunderstorms, not all thunderstorms turn out to be severe due to numerous other processes,” he said. “Machine and deep learning techniques can potentially help forecasters refine their severe weather forecasts to better include not only the storms that ‘talk the talk’ but also ultimately ‘walk the walk,’ particularly when combined with radar-based characteristics.”