🔗 Share this article

When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Forecasting

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

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How The Model Works

The AI system works by identifying trends that conventional time-intensive scientific prediction systems may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, the system is an example of machine learning – a technique that has been employed in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the primary systems that authorities have utilized for years that can take hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity 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 intends to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these forecasts appear highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its techniques – unlike 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 the only one in adopting AI to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.