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

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Through all tropical systems this season, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s Model Works

The AI system works by identifying trends that traditional lengthy physics-based weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could exceed earlier top-tier legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

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

He noted that although the AI is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can make the AI results even more helpful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts appear really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its methods – unlike most systems which are provided at no cost to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

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 better advance warnings of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.