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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity at this time given track uncertainty, that is still plausible.

“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 oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model focused on hurricanes, and currently the initial to beat traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Works

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for a long time – 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 generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

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

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can enhance the DeepMind output more useful for experts by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a peek into its techniques – unlike most other models which are offered free to the public in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.