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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to predict that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, 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 additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way Google’s Model Functions

Google’s model works by spotting patterns that traditional 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,” stated Michael Lowry, a former forecaster.

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

Clarifying AI Technology

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

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for years that can take hours to process and need the largest supercomputers in the world.

Expert Responses and Future Advances

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

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

Franklin said that while the AI is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by offering additional internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these forecasts seem to be really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its techniques – in contrast to nearly all other models which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to address difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.