🔗 Share this article

When Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time given path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How The Model Functions

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been employed in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used 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 Advances

Nevertheless, the fact that Google’s model could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that while the AI is outperforming all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can make the AI results more useful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its techniques – in contrast to most other models which are provided free to the public in their full form by the governments that created and operate them.

Google is not alone in adopting AI to solve difficult meteorological problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.