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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the first to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way Google’s Model Works

The AI system operates through spotting patterns that conventional time-intensive physics-based weather models may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have utilized for years that can require many hours to process and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the AI results more useful for forecasters by offering extra internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these predictions appear highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike most systems which are offered free to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to address difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.