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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new 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 Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. While I am not ready to forecast that strength at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first AI model focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Across all tropical systems this season, Google’s model is the best – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s System Works

Google’s model works by identifying trends that traditional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

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

AI training processes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

He said that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to talk with Google about how it can make the AI results more useful for experts by providing additional under-the-hood data they can utilize to assess exactly why it is producing its answers.

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

Wider Industry Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its methods – unlike most systems which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve difficult weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.