🔗 Share this article

When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. Although I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat traditional meteorological experts at their specialty. Through all tropical systems this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

Google’s model operates through identifying trends that traditional time-intensive scientific weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed 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 such a way that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Still, the fact that Google’s model could outperform earlier top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

He noted that although the AI is outperforming all competing systems on predicting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can enhance the AI results more useful for experts by offering additional internal information they can utilize to evaluate the reasons it is coming up with its conclusions.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Developments

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

The company is not the only one in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.