The National Hurricane Center is gearing up for a major tech upgrade.
Thanks to a new partnership with Google, the U.S. National Oceanic and Atmospheric Administration (NOAA) is beginning to integrate cutting-edge AI tools into its forecasting models for hurricanes and tropical storms. This is being done to help forecasters predict a storm's strength and path faster and more accurately than ever before.
This collaboration couldn't come at a more important time. The NOAA has recently lost access to two key forecasting tools: saildrones, the unmanned hurricane-monitoring boats that the NOAA failed to deploy this year, and the U.S. military's Defense Meteorological Satellite Program (DMSP), which stopped sharing weather data with the NOAA earlier this year.
These cuts resulted in a tightened data pipeline just as hurricane season ramps up.
Google's AI for weather team, including researchers from DeepMind, will now work directly with NOAA forecasters to evaluate, improve, and implement AI-generated storm forecasts in near real time.
The new tools will be integrated into Weather Lab, a platform that makes it easier to visualize complex weather model data. According to Ferran Alet, a research scientist at Google DeepMind, said: "We are honored to collaborate with NOAA ... to enhance their track and intensity forecasts."
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Historically, forecasting tropical cyclones has involved stitching together data from satellites, aircraft, ocean buoys, and radar. This is a time- and resource-intensive process, which the new AI tools plan to accomplish much more quickly. It will allow forecasters to focus on communicating risk to communities and emergency managers.
"The pace of weather modeling innovation is increasing," said Michael Brennan, director of the National Hurricane Center, who emphasized the value of this public-private partnership. The NOAA's forecast teams will also share performance evaluations of the new models with Google to accelerate improvements.
The AI forecasts won't replace human experts any time soon, but they could play a major role in helping people better prepare for extreme weather events. That means more lead time to make decisions and avoid dangerous situations, a win for both public safety and economic resilience.
This collaboration could also carry environmental benefits. More accurate forecasts reduce false alarms and unnecessary travel or emergency responses, potentially lowering fuel use and pollution during storm preparations.
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