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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as 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. No forecaster had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. Although I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to beat traditional meteorological experts at their own game. Across all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

The Way The Model Works

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve relied upon,” he said.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a method that has been employed in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner 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 primary systems that authorities have utilized for years that can take hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

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

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

He noted that while the AI is outperforming all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can enhance the DeepMind output more useful for experts by offering extra under-the-hood data they can use to assess exactly why 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 kind of a opaque process,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its methods – in contrast to most systems which are offered free to the public in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.