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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. 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 recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to predict that strength yet given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system moves slowly over very warm sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to tropical cyclones, and currently the first to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Works

Google’s model works by identifying trends that conventional time-intensive scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, 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 AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

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

AI training processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to run and need the largest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

He said that while the AI is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector 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 techniques – in contrast to nearly all other models which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to solve challenging weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.