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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

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

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am not ready to forecast that strength at this time due to track uncertainty, that is still plausible.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first AI model dedicated to hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Through all tropical systems this season, Google’s model is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

How Google’s Model Works

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

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the reality that the AI could exceed previous top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can use to evaluate the reasons it is producing its answers.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its methods – unlike most other models which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.