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

As the primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued 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 cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. Although I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to hurricanes, and now the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

How Google’s Model Functions

The AI system works by identifying trends that conventional time-intensive scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

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

AI training 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 result, and can do so on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not just chance.”

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to talk with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.