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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model works by identifying trends that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent 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.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in research fields like weather science for a long time – 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 requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for years that can require many hours to process and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not just chance.”

He said that while the AI is beating all other models on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can enhance the AI results more useful for forecasters by offering extra internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its methods – unlike nearly all systems which are provided free to the public in their full form by the governments that designed and maintain them.

The company is not the only one in adopting AI to address challenging meteorological problems. The authorities are developing their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings 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 US weather-observing network.