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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Works

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

“They do it far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that authorities have used for years that can take hours to process and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

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

Franklin said that although Google DeepMind is beating all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the DeepMind output more useful for experts by providing additional internal information they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the system is kind of a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a high-performance weather model which allows researchers a view of its methods – in contrast to most other models which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.