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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted 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. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. While I am not ready to forecast that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model dedicated to hurricanes, and currently the initial to outperform traditional meteorological experts at their specialty. Through all tropical systems this season, Google’s model is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way The Model Functions

The AI system operates through identifying trends that conventional time-intensive physics-based weather models may overlook.

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

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

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 a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have used for years that can require many hours to process and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Still, the reality that Google’s model could exceed previous top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although the AI is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can make the DeepMind output even more helpful for experts by providing additional internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the system is kind of a opaque process,” said Franklin.

Wider Industry 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 systems which are offered free to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting AI to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.