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. The very best forecasting incorporates as much data as possible. There's so much to take in, as the atmosphere is infinitely complex. By using the models and the data we have a snapshot of the most complete look of the atmosphere.
In their study, the researchers analyzed more than 50,000 historical U.S. weather satellite images. In them, experts identified and labeled the shape and motion of ‘comma-shaped’ clouds. These cloud patterns are strongly associated with cyclone formations, which can lead to severe weather events including hail, thunderstorms, high winds and blizzards. Then, using computer vision and machine learning techniques, the researchers taught computers to automatically recognize and detect comma-shaped clouds in satellite images. The computers can then assist experts by pointing out in real time where, in an ocean of data, could they focus their attention in order to detect the onset of severe weather.