Accurate weather predictions are vital for everyone to plan their daily or long term activities. Improving the current model the researchers developed a new technology that can help forecasters recognise potential severe storms more quickly and accurately.
When forecasting weather, meteorologists use a number of models and data sources to track shapes and movements of clouds that could indicate severe storms. However, with increasingly expanding weather data sets and looming deadlines, it is nearly impossible for them to monitor all storm formations especially smaller-scale ones in real time.
Now, there is a computer model that can help forecasters recognise potential severe storms more quickly and accurately, all thanks to a team of researchers who developed a framework based on machine learning linear classifiers a kind of artificial intelligence that detects rotational movements in clouds from satellite images that might have otherwise gone unnoticed, according to the study published in the journal of EEE Transactions on Geoscience and Remote Sensing.
Steve Wistar, the senior forensic meteorologist, said that having this tool to point his eye toward potentially threatening formations could help him to make a better forecast.
"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," he said.
In their study, the researchers worked with Wistar and other meteorologists to analyse more than 50,000 historical U.S. weather satellite images. In them, experts identified and labeled the shape and motion of "comma-shaped" clouds.
These cloud patterns are strongly associated with cyclone formations, which can lead to severe weather events including hail, thunderstorms, high winds, and blizzards.
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Then, using computer vision and machine learning techniques, the researchers taught computers to automatically recognise and detect comma-shaped clouds in satellite images. The computers can then assist experts by pointing out in a real-time where, in an ocean of data, could they focus their attention in order to detect the onset of severe weather.
"Because the comma-shaped cloud is a visual indicator of severe weather events, our scheme can help meteorologists forecast such events," said Rachel Zheng, the main researcher on the project.
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.
"This research is an early attempt to show the feasibility of artificial intelligence-based interpretation of weather-related visual information to the research community. 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," James Wang, one of the researchers of the study.
"The benefit [of this research] is calling the attention of a very busy forecaster to something that may have otherwise been overlooked," concluded Wistar.