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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and now the first to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, possibly saving 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 physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Future Developments

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

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of chance.”

He said that while the AI is outperforming all other models on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra under-the-hood data they can use to assess the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the output of the model is essentially a black box,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a view of its methods – in contrast to most systems which are provided free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.