The same artificial intelligence technique typically used in facial recognition systems could help improve prediction of hailstorms and their severity, according to a new study from the National Center for Atmospheric Research (NCAR).
Instead of zeroing in on the features of an individual face, scientists trained adeep learning modelcalled a convolutional neural network to recognize features of individual storms that affect the formation of hail and how large the hailstones will be, both of which are notoriously difficult to predict.
The promising results, published in the American Meteorological Society’sMonthly Weather Review, highlight the importance of taking into account astorm‘s entire structure, something that’s been challenging to do with existing hail-forecasting techniques.
“We know that the structure of a storm affects whether the storm can produce hail,” said NCAR scientist David John Gagne, who led the research team. “A supercell is more likely to produce hail than a squall line, for example. But most hail forecasting methods just look at a small slice of the storm and can’t distinguish the broader form and structure.”
The research was supported by the National Science Foundation, which is NCAR’s sponsor.
“Hail—particularly large hail—can have significant economic impacts on agriculture and property,” said Nick Anderson, an NSF program officer. “Using these deep learning tools in unique ways will provide additional insight into the conditions that favor large hail, improvingmodel predictions. This is a creative, and very useful, merger of scientific disciplines.”