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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into 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 coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs show Melissa reaching a most intense hurricane. Although I am not ready to forecast that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform standard weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The System Functions

The AI system works by identifying trends that conventional time-intensive scientific prediction systems may overlook.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a method that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that while these forecasts seem to be highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its methods – in contrast to most other models which are offered free to the public 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 solve challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.