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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 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 most intense storm. Although I am not ready to forecast that intensity at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and now the initial to outperform traditional weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How Google’s System Works

The AI system operates through identifying trends that conventional time-intensive physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that authorities have used for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the fact that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of chance.”

He said that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all systems which are offered at no cost to the public in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use AI to solve difficult weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown better performance over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.