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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, 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 initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their own game. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave residents extra time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model works by identifying trends that traditional time-intensive scientific weather models may miss.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Reactions and Future Developments

Still, the fact that the AI could exceed earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of chance.”

He said that while the AI is outperforming all competing systems on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by providing extra internal information they can use to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a peek into its methods – in contrast to nearly all other models which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in adopting AI to solve difficult meteorological problems. The US and European governments also have their respective AI weather models 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 difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the national monitoring system.