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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense storm. While I am unprepared to predict that intensity at this time given track uncertainty, 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 meteorological experts at their own game. Through all tropical systems so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries 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 people and assets.

The Way Google’s Model Works

Google’s model operates through identifying trends that conventional time-intensive scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have utilized for years that can take hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Still, the fact that the AI could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering extra internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these forecasts seem to be really, really good, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a peek into its methods – unlike nearly all other models which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.