When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms.
When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms. However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations — especially smaller-scale ones — in real time.
Now, there is a computer model that can help forecasters recognize potential severe storms more quickly and accurately, thanks to a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain. They have developed a framework based on machine learning linear classifiers — a kind of artificial intelligence — that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed. This AI solution ran on the Bridges supercomputer at the Pittsburgh Supercomputing Center.
Steve Wistar, senior forensic meteorologist at AccuWeather, said that having this tool to point his eye toward potentially threatening formations could help him to make a better forecast.
“The very best forecasting incorporates as much data as possible,” he said. “There’s so much to take in, as the atmosphere is infinitely complex. By using the models and the data we have [in front of us], we’re taking a snapshot of the most complete look of the atmosphere.”
Read more at Penn State
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