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When Tropical Storm Melissa swirled 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 just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense storm. While I am unprepared to forecast that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How Google’s Model Functions

The AI system operates through spotting patterns that traditional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

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

Machine learning takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists 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 chance.”

He said that although the AI is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that although these forecasts seem to be highly accurate, the output of the system is kind of a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a top-level weather model which allows researchers a peek into its techniques – unlike nearly all systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not the only one in starting to use AI to address difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.