🔗 Share this article

When Tropical Storm Melissa was churning south 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 become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

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 certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer AI model focused on tropical cyclones, and now the first to beat standard meteorological experts at their own game. Across all tropical systems this season, the AI is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Works

Google’s model operates through identifying trends that traditional lengthy scientific weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said 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 weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that Google’s model could exceed previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while the AI is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with the company about how it can make the AI results more useful for experts by providing additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a top-level weather model which allows researchers a view of its techniques – in contrast to nearly all other models which are offered at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.