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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as 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 coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. While I am not ready to predict that strength yet due to path variability, that remains a possibility.

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

Outperforming Traditional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Functions

Google’s model operates through identifying trends that traditional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example 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 such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Expert Reactions and Future Developments

Still, the reality that the AI could exceed previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that while the AI is outperforming all other models on forecasting the future path of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by providing 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 forecasts seem to be really, really good, the results of the system is kind of a black box,” remarked Franklin.

Wider Industry Developments

There has never been 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 at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.