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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am not ready to forecast that intensity at this time given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their specialty. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way The Model Functions

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and need the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that although the AI is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can make the DeepMind output even more helpful for experts by offering extra internal information they can utilize to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a peek into its methods – in contrast to most systems which are provided free to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.