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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology 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 astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first AI model focused on tropical cyclones, and now the first to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The System Functions

The AI system operates through spotting patterns that conventional time-intensive scientific weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives 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 although Google DeepMind is beating all other models on forecasting the trajectory of storms globally this year, like many AI models 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 above the Caribbean.

In the coming offseason, he stated he intends to discuss with Google about how it can enhance the AI results more useful for experts by offering additional internal information they can use to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that although these predictions appear highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided free to the public in their full form by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.