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

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to predict that intensity yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm drifts over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the initial to outperform standard weather forecasters at their own game. Through all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, the system is an example of AI training – a technique that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have utilized for years that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he plans to talk with the company about how it can enhance the AI results more useful for experts by providing extra internal information they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that nags at me is that while these forecasts appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its methods – in contrast to nearly all other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies tackling previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.