Technology 3 min read

New AI Algorithm Detects Cloud Formations that Lead to Storms

WikimediaImages / Pixabay

WikimediaImages / Pixabay

Weather forecasting requires meteorologists to depend on models and data sources for tracking the shape and movement of clouds. That way, they can detect a severe storm ahead.

But, the data set has been increasing with advancements in technology. When you combine this with a looming deadline, it becomes almost impossible to monitor all storm formations in real-time. This is especially true for the small-scale storms.

Well, that’s about to change.

Thanks to a computer model developed by a team of researchers at Penn State, AccuWeather, Inc., and the University of Almería in Spain, forecasters can now spot potential storms faster and with higher precision.

The team developed a framework based on a kind of artificial intelligence called machine learning linear classifiers. The AI solution, which ran on Bridges supercomputer, could detect rotational movement from satellite images.

In other words, the tool can find potentially threatening formation and would enable forecasters to make better weather forecasts.

Speaking about the AI algorithm, a senior forensic meteorologist at Accuweather, Steve Wistar said:

“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 [in front of us], we’re taking a snapshot of the most complete look of the atmosphere.”

How the AI Algorithm Detects Cloud Formations

Together with AccuWeather meteorologists, Wistar and a team of researchers analyzed over 50,000 historical U.S. weather satellite images. During the analysis, the team identified and labeled the shape as well as the motion of “comma-shaped” clouds.

Past studies already suggest the link between these cloud patterns and cyclone formation, which results in severe weather events. These include high winds, thunderstorm, blizzards, and hail.

Using machine learning techniques and computer vision, researchers taught computers to detect the comma-shaped cloud in satellite images automatically. That way, the machine can quickly scan an ocean of data and help meteorologists identify in real-time where to focus their attention.

Doctoral student in the College of Information Sciences and Technology at Penn State and the lead researcher of the study, Rachel Zheng, said:

“Our method can capture most human-labeled, comma-shaped clouds. Moreover, our method can detect some comma-shaped clouds before they are fully formed, and our detections are sometimes earlier than human eye recognition.”

With 99 percent speed and an average of 40 seconds per prediction, the AI algorithm may be the most effective method of detecting comma-shaped clouds. It also outperformed current severe-weather detection methods by predicting 64 percent of severe weather events.

A computerized system analyzing and learning from available data can help forecasters provide a quick and accurate interpretation. This is particularly useful in time-sensitive situations.

“The calling of our business is to save lives and protect property,” said Wistar. “The more advanced notice to people that would be affected by a storm, the better we’re providing that service. We’re trying to get the best information out as early as possible.”

Read More: Why Weather Manipulation Might Be Real

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Sumbo Bello

Sumbo Bello is a creative writer who enjoys creating data-driven content for news sites. In his spare time, he plays basketball and listens to Coldplay.

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