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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. While I am unprepared to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the first to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided residents extra time to get ready for the catastrophe, potentially preserving people and assets.

The Way The System Works

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

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the newcomer 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 said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform previous top-tier legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s evident this is not just beginner’s luck.”

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts seem to be really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – unlike nearly all other models which are offered at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.