🔗 Share this article

As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am unprepared to predict that strength yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Functions

The AI system works by spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that the AI could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

He noted that although the AI is outperforming all other models on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the AI results even more helpful 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 nags at me is that although these forecasts appear highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Wider Industry Trends

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

The company is not the only one in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.