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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

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 key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense storm. Although I am not ready to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model focused on hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

The Way The Model Works

Google’s model operates through identifying trends that conventional lengthy scientific weather models may overlook.

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

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could outperform earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense storms.

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

He said that while the AI is outperforming all competing systems on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can enhance the AI results more useful for experts by providing additional internal information they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its techniques – in contrast to most other models which are offered 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 adopting AI to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.