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As Tropical Storm Melissa swirled 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 forecasted that in just 24 hours the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 hurricane. While I am not ready to forecast that intensity at this time given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the initial to beat traditional meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s System Works

Google’s model works by spotting patterns that conventional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former forecaster.

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

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

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

Franklin said that while Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to talk with the company about how it can make the AI results more useful for forecasters by providing extra under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its techniques – unlike most systems which are offered free to the general audience in their full form by the governments that created and operate them.

The company is not alone in starting to use AI to address challenging meteorological problems. The US and European governments also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.