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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence 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 remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am not ready to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to outperform standard meteorological experts at their own game. Through all tropical systems this season, Google’s model is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way The System Functions

The AI system operates through identifying trends that conventional lengthy physics-based prediction systems may overlook.

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

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin noted that while the AI is beating all competing systems on predicting the trajectory of hurricanes worldwide 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 also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by offering extra under-the-hood data they can use to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a peek into its methods – in contrast to nearly all systems 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 adopting AI to address difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the US weather-observing network.