Meteorologists use a number of models and data sources to track the forms and movements of clouds that may indicate severe storms. Therefore, it is difficult to observe all the storm’s formations, scientists are now turning to artificial intelligence AI for a better outcome to predict bad weather.
According to the scientific phys, a computer model is now available that can help meteorologists identify the potential severe storms more quickly and accurately, thanks to a team of researchers in Pennsylvania, AccuWeather Inc and Almeria University in Spain.
They developed a framework based on a type of artificial intelligence, which detects rotational movements in cloud satellite images that may not have been noticed.
Steve Wistar, senior forensic meteorologist at AccuWeather, said the presence of this tool to draw its attention to structures that could be threatened could help to create better predictions.
He explained that the best kind of prediction is that includes as much data as possible, as the atmosphere is endless complex, using models and data and analysis through AI, can take a quick look at the most complete form of the atmosphere.
The test observed more than 50,000 weather satellite images in the United States, where experts identified patterns of clouds strongly linked to hurricane formation that could lead to severe climate events, including cold, thunderstorms, high winds and snowstorms.
This project enhances earlier work between AccuWeather and a College of IST research group led by professor James Wang, who is the dissertation adviser of Zheng.
“We recognized when our collaboration began [with AccuWeather in 2010] that a significant challenge facing meteorologists and climatologists was in making sense of the vast and continually increasing amount of data generated by Earth observation satellites, radars and sensor networks,” said Wang. “It is essential to have computerized systems analyze with the help of artificial intelligence and learn from the data so we can provide timely and proper interpretation of the data in time-sensitive applications such as severe-weather forecasting.”