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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. While I am not ready to predict that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How The System Works

The AI system works by identifying trends that conventional lengthy scientific weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

To be sure, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can take hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin said that while the AI is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company about how it can make the AI results even more helpful for forecasters by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a top-level weather model which grants experts a peek into its methods – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use AI to solve difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.