🔗 Share this article

As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am not ready 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 moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How Google’s Model Works

Google’s model works by identifying trends that traditional lengthy scientific weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that while the AI is beating all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity predictions 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 plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering extra internal information they can use to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these forecasts seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are offered 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 address difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.