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

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the first to outperform standard weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the catastrophe, potentially preserving lives and property.

How The System Works

The AI system works by spotting patterns that traditional time-intensive scientific prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only requires minutes to generate an result, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Still, the reality that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

He said that although Google DeepMind is outperforming all other models on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 above 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 providing additional under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that while these predictions appear really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a view of its methods – unlike nearly all systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.