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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 a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form 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 tore through Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense storm. While I am not ready to predict that intensity at this time due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Works

The AI system works by spotting patterns that conventional time-intensive scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

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

He noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can make the DeepMind output even more helpful for experts by providing extra under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear highly accurate, the output of the system is kind of a opaque process,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – unlike most systems which are offered at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the national monitoring system.