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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

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 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s System Functions

Google’s model operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

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

He said that although Google DeepMind is beating all other models on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to assess exactly why it is coming up with its answers.

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

Wider Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to most other models which are offered free to the public in their full form by the authorities that created and operate them.

Google is not alone in starting to use AI to address difficult meteorological problems. The authorities also have their own AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.