A team of researchers at Penn State, AccuWeather and the University of Almeria in Spain have developed a framework based on artificial intelligence that detects weather rotational movements in clouds from satellites images.
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 researchers worked with Wistar and other AccuWeather meteorologists to analyze more than 50,000 historical U.S. weather satellite images. In them, experts identified and labeled the shape and motion of “comma-shaped” clouds.
Then, using computer vision and machine learning techniques, the researchers taught computers to automatically recognize and detect comma-shaped weather satellites 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.
The researchers found that their method can effectively detect comma-shaped clouds with 99 percent accuracy, at an average of 40 seconds per prediction. It was also able to predict 64 percent of severe weather events, outperforming other existing severe-weather detection methods.
More research to integrate this approach with existing numerical weather-prediction models and other simulation models will likely make the weather forecast more accurate and useful to people.