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As Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence 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: “Roughly 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am not ready to forecast that intensity yet given path variability, that is still plausible.

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

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the first to outperform standard weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The System Functions

The AI system operates through spotting patterns that traditional time-intensive scientific prediction systems may overlook.

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

“This season’s events has demonstrated in short order 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,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for decades that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

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

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the system is essentially a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – in contrast to nearly all systems which are provided at no cost to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.