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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

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 confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense hurricane. While I am not ready to forecast that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm drifts over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s Model Works

The AI system operates through identifying trends that conventional lengthy physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its model only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can require many hours to run and require the largest high-performance systems in the world.

Expert Responses and Future Advances

Still, the reality that the AI could outperform previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

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

He noted that although the AI is beating all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can enhance the DeepMind output more useful for experts by providing additional under-the-hood data they can use to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the output of the model is kind of a opaque process,” 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 most other models which are provided free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.