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

As the primary meteorologist 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 Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. Although I am unprepared to predict that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving people and assets.

How The Model Works

Google’s model operates through identifying trends that conventional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can take hours to run and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin said that while the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can enhance the DeepMind output more useful for experts by providing additional internal information they can use to evaluate the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a top-level weather model which grants experts a view of its techniques – in contrast to most other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.