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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to beat standard meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way Google’s Model Works

The AI system operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have used for years that can require many hours to run and need the largest high-performance systems in the world.

Expert Responses and Future Advances

Still, the fact that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not just chance.”

He said that while the AI is beating all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin 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 Google about how it can make the AI results even more helpful for forecasters by providing extra internal information they can utilize to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts appear highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Wider Sector Developments

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

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.