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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters 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 AI simulation runs show Melissa becoming a most intense hurricane. While I am not ready to predict that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model focused on hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The System Works

Google’s model works by spotting patterns that traditional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have utilized for years that can take hours to process and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

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

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the DeepMind output more useful for experts by providing extra internal information they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a top-level weather model which grants experts a view of its techniques – unlike nearly all systems which are provided at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting AI to solve difficult meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.