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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model focused on tropical cyclones, and now the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way Google’s Model Works

The AI system works by spotting patterns that conventional lengthy physics-based weather models may overlook.

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

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

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

AI training takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin said that although the AI is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to talk with the company about how it can make the AI results even more helpful for experts by offering extra internal information they can use to assess the reasons it is coming up with its conclusions.

“The one thing that nags at me is that although these forecasts appear really, really good, the results of the system is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – unlike nearly all other models which are offered free to the general audience 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 respective AI weather models in the works – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.