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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am unprepared to predict that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the storm drifts over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s System Functions

Google’s model works by identifying trends that conventional time-intensive scientific prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a method that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments have utilized for years that can take hours to process and require the largest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could exceed previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

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

He noted that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can enhance the AI results even more helpful for experts by providing extra under-the-hood data they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the output of the system is essentially a black box,” said Franklin.

Wider Industry Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its techniques – in contrast to most systems which are provided 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 adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.