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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

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

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that strength yet due to path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Works

Google’s model operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a method that has been employed in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the fact that Google’s model could exceed previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can use to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these forecasts appear highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike most other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in adopting AI to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the national monitoring system.