Facial recognition technique could improve hail forecasts

phys.org | 7/30/2019 | Staff
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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 a deep learning model called 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.

Results - Meteorological - Society - Monthly - Weather

The promising results, published in the American Meteorological Society's Monthly Weather Review, highlight the importance of taking into account a storm'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."

Research - National - Science - Foundation - NCAR

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, improving model predictions. This is a creative, and very useful, merger of scientific disciplines."

Criteria - Size - Hailstones - Path - Hailstones

But even when all these criteria are met, the size of the hailstones produced can vary remarkably, depending on the path the hailstones travel through the storm and the conditions along that path. That's where storm structure comes into play.

"The shape of the storm is really important," Gagne said. "In the past we have tended to focus on single points in a storm or vertical...
(Excerpt) Read more at: phys.org
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