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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way The System Works

The AI system works by spotting patterns that conventional lengthy scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

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

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could outperform previous gold-standard legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

He noted that although the AI is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on 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.

In the coming offseason, he stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional 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 seem to be highly accurate, the results of the system is essentially a black box,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike most other models which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not alone in starting to use AI to solve challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.