Specifically, a team of researchers from Penn State University in the U.S., the University of Almería in Spain and AccuWeather Inc. based their device on a form of artificial intelligence, also known as machine learning classifiers.

This computer model, according to the researchers, is able to detect rotational movements in clouds from satellite images that might, in other situations, been unnoticed.

Steve Wistar, senior forensic meteorologist at AccuWeather commented

The very best forecasting incorporates as much data as possible ... 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.

In order to precisely adjust their system, the researchers used more than 50,000 weather satellite images taken over the years of the United States so as to track so-called comma-shaped clouds. These clouds are specific distribution patterns that are strongly associated with the cyclone formulation and can trigger thunderstorms, blizzards and gusty winds.

Moreover, 'Sustainability Times' added that researchers taught computers to identify and detect such clouds in satellite images. This computer-aided process can help meteorologists pinpoint the clouds in reams of real-live data so they can monitor them more closely and predict severe weather events with greater speed and accuracy.

Additionally, the new system can detect comma-shaped clouds with 99% accuracy at an average of 40 seconds per prediction.

By being able to predict nearly two-thirds of severe weather events, it can outperform other severe-weather detection methods.

For the time being, to forecast the weather, meteorologists rely their research on computer models and various up-to-date data such as air temperatures and air speeds. They also track the formation and movement of clouds to see how and where downpours or storms might develop.