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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 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 Category 5 storm. While I am not ready to predict that intensity at this time given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and now the initial to outperform traditional weather forecasters at their own game. Through all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Functions

Google’s model operates through identifying trends that conventional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data 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 strong contrast to the primary systems that governments have used for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin said that although Google DeepMind is outperforming all other models on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can make the AI results more useful for forecasters by offering extra internal information they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has developed a top-level forecasting system which grants experts a view of its methods – unlike nearly all systems which are provided at no cost to the general audience in their full form by the governments that created and operate them.

The company is not alone in starting to use AI to solve difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.