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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the storm 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: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the initial to beat traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s System Functions

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can do so on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

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

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

He said that although the AI is beating all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he said he plans to talk with the company about how it can enhance the AI results more useful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is essentially a black box,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are provided at no cost to the general audience in their full form by the governments that created and operate them.

Google is not alone in adopting AI to address difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve 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. One company, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.