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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

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

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

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

Outperforming Traditional Systems

The AI model is the first AI model focused on hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

How The Model Works

The AI system works by identifying trends that traditional 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 time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like meteorology for years – and is not generative AI 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 generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the fact that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

He noted that although the AI is beating all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can make the AI results even more helpful for forecasters by offering extra internal information they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions seem to be highly accurate, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a view of its methods – in contrast to nearly all systems which are offered free to the general audience in their full form by the authorities that created and operate them.

The company is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

The next steps in AI weather forecasts 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 have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.