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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise 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 remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How Google’s System Functions

Google’s model operates through spotting patterns that conventional time-intensive scientific prediction systems may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models 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 Responses and Future Developments

Nevertheless, the reality that the AI could exceed earlier gold-standard legacy models so quickly is truly remarkable to meteorologists 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 just chance.”

Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its methods – unlike nearly all systems which are offered at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.