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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence 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 start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI simulation runs show Melissa becoming a most intense storm. Although I am not ready to forecast that intensity yet due to path variability, that remains a possibility.

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

Surpassing Conventional Models

The AI model is the first AI model dedicated to hurricanes, and now the first to beat standard meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Functions

The AI system operates through spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, 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.

AI training processes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

Still, the fact that the AI could outperform earlier gold-standard traditional systems so quickly 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 former forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He noted that although the AI is outperforming all other models on predicting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can make the AI results more useful for experts by providing extra under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions appear highly accurate, the results of the model is essentially a black box,” remarked Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – unlike nearly all other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.