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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that intensity at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model dedicated to hurricanes, and currently the first to beat standard meteorological experts at their specialty. Through all tropical systems so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

How The Model Works

The AI system operates through identifying trends that conventional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can require many hours to process and require some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Still, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin noted that although Google DeepMind is beating all other models on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results more useful for experts by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a top-level weather model which allows researchers a peek into its methods – unlike nearly all other models which are offered at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.