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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. Although I am unprepared to predict that intensity yet given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model dedicated to hurricanes, and currently the first to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s Model Works

The AI system operates through spotting patterns that traditional time-intensive physics-based weather models may overlook.

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

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Still, the reality that Google’s model could exceed previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

Franklin said that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can use to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that although these forecasts appear really, really good, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a view of its methods – in contrast to most systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in adopting AI to address challenging meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.