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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first AI model focused on hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Through all tropical systems this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

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

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns 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 desktop computer – in strong contrast to the primary systems that governments have used for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

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

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

Franklin said that while the AI is beating all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can enhance the AI results even more helpful for forecasters by offering extra under-the-hood data they can utilize to assess exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts appear really, really good, the results of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its techniques – unlike most other models which are provided free to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities also have their respective AI weather models in the works – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.