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

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

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 confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am unprepared to predict that strength yet due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, the AI is the best – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, possibly saving lives and property.

The Way Google’s System Works

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – 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 requires minutes to generate an answer, 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 the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that the AI could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while the AI is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can make the AI results more useful for forecasters by providing additional under-the-hood data they can utilize to evaluate the reasons it is producing its answers.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all systems which are offered at no cost to the general audience in their full form by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.