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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided residents extra time to get ready for the disaster, potentially preserving lives and property.

The Way The System Works

The AI system works by identifying trends that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can take hours to process and need the largest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that although the AI is beating all competing systems on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by providing additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that while these predictions seem to be really, really good, the output of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its techniques – in contrast to nearly all systems which are provided free to the public in their entirety by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.