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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 storm. Although I am unprepared to forecast that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on hurricanes, and now the first to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How Google’s Model Works

Google’s model operates through identifying trends that traditional time-intensive physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets 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 desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin said that although the AI is beating all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a high-performance weather model which grants experts a peek into its methods – in contrast to most systems which are provided at no cost to the public in their full form by the authorities that created and operate them.

Google is not alone in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.