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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

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 in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength at this time given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the first to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, the AI is the best – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls 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 people and assets.

How The System Works

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

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training 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 do so on a desktop computer – in sharp difference to the primary systems that governments have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of chance.”

He noted that while the AI is outperforming all other models on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions appear highly accurate, the output of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in adopting AI to solve difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.