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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

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

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 storm. Although I am unprepared to forecast that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the system drifts over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial 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.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The Model Works

The AI system operates through spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for years that can require many hours to run and require the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could exceed previous top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just chance.”

He noted that while the AI is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to talk with the company about how it can make the DeepMind output more useful for experts by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“The one thing that nags at me is that while these predictions appear really, really good, the results of the model is essentially a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all systems which are provided at no cost to the public in their entirety by the authorities that created and operate them.

The company is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.