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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster 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. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Predictions

Meteorologists 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 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. While I am unprepared to predict that intensity yet given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s System Works

Google’s model works by identifying trends that traditional lengthy physics-based prediction systems may miss.

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

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the reality that Google’s model could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

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

Franklin said that although the AI is beating all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength 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 said he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering additional internal information they can utilize to assess exactly why it is coming up with its answers.

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

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level weather model which grants experts a view of its methods – in contrast to nearly all systems which are provided free to the general audience in their full form by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to address 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 AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.