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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 hurricane. While I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Across all tropical systems so far this year, the AI is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

The Way The Model Works

Google’s model operates through identifying trends that traditional time-intensive scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for years that can require many hours to process and need the largest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that Google’s model could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that while the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he plans to talk with the company about how it can enhance the DeepMind output even more helpful 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 although these forecasts appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a view of its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting AI to solve challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.