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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Forecasting

Meteorologists 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 certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the initial to beat standard meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way Google’s System Works

The AI system works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said 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, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

Franklin noted that although Google DeepMind is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing additional internal information they can use to evaluate exactly why it is producing its answers.

“The one thing that troubles me is that while these predictions appear really, really good, the output of the system is essentially a black box,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – unlike most systems which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to address difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.