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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that intensity at this time due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the first to outperform traditional weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

How The Model Functions

The AI system operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

To be sure, the system is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering extra internal information they can utilize to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the results of the system is essentially a black box,” said Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – unlike most other models which are offered free to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.