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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a Category 5 storm. Although I am not ready to forecast that intensity yet given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The Model Works

Google’s model operates through spotting patterns that traditional lengthy scientific weather models may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can take hours to run and require the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the reality that the AI could exceed previous gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all other models on predicting the future path of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification 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 more useful for experts by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the results of the model is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its techniques – unlike most 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 starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.