MIAMI – This year’s hurricane season marks a significant turning point in weather forecasting with artificial intelligence models being integrated into the analytical toolkit of forecasters at the National Hurricane Center.
While not yet part of the official consensus models, AI is poised to dramatically enhance the accuracy and efficiency of hurricane predictions, ushering in a new era of meteorological technology.
“This is going to be a year where we start to really take in and evaluate some of the AI model guidance from various producers, and we’re going to pull that into our systems here and evaluate it,” NHC Director Michael Brennan told FOX Weather.
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An AI-powered weather forecasting system analyzes global data to predict extreme weather events. (Generated with AI via Adobe Stock Images)
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The NHC will then compare AI model guidance to their traditional models to see how well it performs and how the agency can integrate it into its forecasting process.
“They’re not going be part of our official consensus or blended models this year, but we may make some additional ones on the side that we’re going to test out and see how they do and perform,” Brennan adds. “I think especially for track, there’s a lot of promise on the AI side for some potential improvements in the near term.”
Brennan notes that intensity forecasting might take longer to fully mature with AI.
“You have to think of them all as being in experimental mode, and I think it’s important actually to distinguish between them,” he said.
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A new breed of AI models
For years, meteorologists have relied on sophisticated forecast models that simulate atmospheric conditions through complex mathematical equations, according to FOX Weather Hurricane Specialist Bryan Norcross.
These traditional models, like the American GFS and European ECMWF, require immense computational power, running for hours to produce a single forecast, Norcross said. However, a new breed of AI models, such as Google’s Graphcast and the European Center’s AIFS, are emerging as powerful complements.
Unlike their traditional counterparts, AI models utilize pattern recognition, learning from vast datasets of past weather phenomena to predict future outcomes, according to Norcross. This approach allows them to operate with remarkable speed, generating forecasts in mere minutes.
“This year is going to be the big year because now we have access and regular access, immediate access, to many different AI models,” he explained.
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FILE – Computer screens at the National Weather Service office in Shreveport, Louisiana, track the progress of Hurricane Katrina as it approaches the Louisiana coastline on August 28, 2005.
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What’s the difference between the AI models and the regular models?
According to Norcross, the regular model tries to simulate the atmosphere. The AI models don’t do that and are more pattern recognition oriented.
However, the speed and efficiency of AI models allow for an unprecedented number of “runs” or simulations, providing forecasters with a much broader distribution of possible outcomes, Norcross adds. This increased variability insight is crucial for understanding the potential range of a hurricane’s behavior.
“Because the AI models are so efficient, what you can do is you can run the AI model with exactly the same information, and then you can modify it a little bit and run it again, and you can really get a good distribution of possibilities because you don’t have to use all this computer power,” Norcross said.
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Currently, NHC forecasters rely on a consensus approach, averaging the outputs of multiple traditional models to produce the most reliable forecast, Norcross adds. This consensus has consistently proven to be more accurate than any single model.
“This year, the AI models are not going to be added to the consensus, but they will in the future,” Norcross said. “They’ll do this in the background and just see if it helps the consensus or not.”