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When Developing Cyclone Melissa swirled 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 forecasted that in just 24 hours the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that strength yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the first to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How Google’s System Functions

Google’s model operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

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

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

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 not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that Google’s model could exceed previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not just chance.”

He said that while the AI is outperforming all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can use to evaluate the reasons it is producing its answers.

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

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are offered free to the public in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.