🔗 Share this article

When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs show Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength yet given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s System Works

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

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex 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 traditional forecasting tools we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Developments

Nevertheless, the fact that Google’s model could exceed previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of chance.”

Franklin said that although Google DeepMind is beating all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can utilize to evaluate exactly why it is producing its answers.

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

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to nearly all other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to address challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.