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

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. While I am unprepared to forecast that intensity at this time due to path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How Google’s System Functions

Google’s model works by identifying trends that conventional lengthy scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that Google’s model could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the AI results more useful for forecasters by providing extra internal information they can use to assess exactly why it is producing its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike nearly all other models which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting AI to address difficult weather forecasting problems. The authorities also have their respective 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 tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.