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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 storm. Although I am not ready to forecast that strength yet given track uncertainty, that remains a possibility.

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

Outperforming Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. The confident prediction likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way The System Works

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

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can take hours to run and need the largest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin said that while the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to assess exactly why it is producing its answers.

“A key concern that nags at me is that while these forecasts appear really, really good, the results of the system is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a top-level forecasting system which grants experts a view of its techniques – unlike most other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

The company is not alone in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.