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As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

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

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. While I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and now the first to outperform standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How The Model Works

Google’s model works by identifying trends that conventional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

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

He noted that although the AI is beating all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that troubles me is that while these predictions appear really, really good, the output of the system is kind of a opaque process,” said Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – in contrast to most systems which are offered at no cost to the public in their entirety by the authorities that created and operate them.

The company is not the only one in adopting AI to solve difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.