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

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. Although I am not ready to predict that strength at this time due to path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on path forecasts.

The hurricane 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 region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

How The System Works

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

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

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology 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 such a way that its system only takes a few minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and require the largest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

He noted that while the AI is outperforming all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can make the AI results more useful for experts by offering additional internal information they can utilize to assess the reasons it is coming up with its answers.

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

Broader Industry Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – unlike most systems which are offered free to the general audience in their full form by the authorities that created and operate them.

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

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.