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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense storm. Although I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model works by identifying trends that traditional lengthy scientific prediction systems may overlook.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of chance.”

Franklin noted that although the AI is outperforming all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to talk with Google about how it can enhance the AI results more useful for experts by providing extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is kind of a black box,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a top-level weather model which grants experts a peek into its methods – in contrast to most systems which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.