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

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s Model Works

Google’s model works by identifying trends that conventional lengthy physics-based weather models may overlook.

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

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to discuss with Google about how it can make the AI results even more helpful for experts by providing additional internal information they can utilize to assess exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike nearly all systems which are provided at no cost to the public in their full form by the governments that designed and maintain them.

Google is not the only one in starting to use AI to address difficult weather forecasting problems. The authorities also have their own AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.