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When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched 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 Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. While I am unprepared to predict that intensity at this time due to path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How The System Works

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends 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 strong contrast to the primary systems that governments have used for years that can require many hours to run and require the largest high-performance systems in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering extra internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear highly accurate, the output of the model is kind of a black box,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its methods – in contrast to nearly all other models which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.