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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense storm. Although I am unprepared to predict that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the first to outperform standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving lives and property.

How The System Works

The AI system operates through identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can take hours to process and require some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense storms.

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

He said that although Google DeepMind is beating all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts seem to be 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 most systems which are provided at no cost to the public in their entirety by the authorities that created and operate them.

Google is not the only one in adopting AI to address challenging meteorological problems. The authorities also have their respective AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.