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

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: 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 astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason 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 yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s Model Works

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

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

He noted that although the AI is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can enhance the DeepMind output even more helpful for experts by offering extra internal information they can utilize to assess exactly why it is producing its answers.

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

Broader Sector Trends

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to nearly all other models which are provided free to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.