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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Functions

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data 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 strong contrast to the primary systems that governments have used for decades that can take hours to process and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

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

He said that although the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that troubles me is that although these forecasts appear really, really good, the results of the system is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been 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 systems which are offered at no cost to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.