🔗 Share this article

When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the initial to beat standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

How The System Works

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

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

“What this hurricane season has proven in quick time is that the recent AI weather models are on par with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for years that can take hours to process and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that the AI could outperform earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast 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 beginner’s luck.”

He noted that although Google DeepMind is beating all other models on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the AI results more useful for experts by providing extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the results of the model is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a top-level weather model which allows researchers a view of its methods – in contrast to nearly all other models which are provided free to the public in their full form by the authorities that created and operate them.

The company is not the only one in starting to use artificial intelligence to address challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.