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

As the primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon 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 unprepared to predict that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and now the initial to outperform traditional meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, the AI is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How Google’s Model Works

Google’s model works by identifying trends that conventional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can require many hours to process and require the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the reality that the AI could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with the company about how it can make the AI results even more helpful for experts by providing additional internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that although these predictions appear highly accurate, the output of the model is kind of a black box,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – unlike most systems which are offered at no cost to the public in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.