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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence 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 key factor for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense storm. Although I am unprepared to predict that intensity at this time given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to beat standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s System Functions

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

He said that while the AI is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions appear really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a view of its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments 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 artificial intelligence predictions appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.