Researchers have developed an algorithm by using Artificial Intelligence (AI), to detect cloud formations which lead to storms, hurricanes and cyclones.

According to the study, published in the journal IEEE Transactions on Geoscience and Remote Sensing, a model that can help forecasters identify probable severe storms more quickly and perfectly.

The researchers prepared a framework based on Machine Learning (ML) - a type of Artificial Intelligence which spots rotational movements in clouds from satellite images that may have otherwise gone unnoticed.

"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, we're taking a snapshot of the most complete look of the atmosphere," said Steve Wistar, Senior Forensic Meteorologist at AccuWeather in the US.

For this research, researchers evaluated above 50,000 US weather satellite images and recognized and labelled the shape and motion of 'comma-shaped' clouds.

These cloud patterns are strongly linked with cyclone formations which can cause serious weather events including hail, thunderstorms, high winds and blizzards, they said.

Moreover, by using computer vision and artificial intelligence techniques, the researchers taught computers to automatically recognize and spot 'comma-shaped' clouds in satellite images.

At that point, utilizing Artificial Intelligence's PC vision and ML strategies, the specialists instructed PCs to consequently perceive and recognize 'comma-formed' mists in satellite pictures.

The computers could then help experts by indicating in immediately where, in an ocean of data, could they focus their attention so as to detect the beginning of severe weather.

"Because the 'comma-shaped' cloud is a visual indicator of severe weather events, our scheme can help meteorologists to forecast such events," said study lead author Rachel Zheng from Penn State University in the US.

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 capable to predict 64 percent of severe weather events, outperforming other existing severe weather detection methods.

This research is an early understanding of weather-related visual information.