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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to beat traditional meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave residents extra time to prepare for the catastrophe, possibly saving lives and property.

How Google’s System Works

Google’s model operates through identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he added.

Understanding AI Technology

To be sure, the system is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets 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 utilized for decades that can take hours to process and require some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that Google’s model could exceed earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

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

He noted that while Google DeepMind is outperforming all competing systems on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by offering extra internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that although these predictions appear highly accurate, the results of the model is kind of a black box,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered at no cost to the general audience in their full form by the governments that created and operate them.

The company is not the only one in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the national monitoring system.