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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and currently the first to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided residents extra time to prepare for the disaster, possibly saving lives and property.

How The System Functions

Google’s model operates through identifying trends that traditional time-intensive physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

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

AI training processes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just chance.”

Franklin noted that while the AI is beating all other models on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to talk with the company about how it can make the DeepMind output even more helpful for forecasters by providing extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

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

Wider Industry Developments

Historically, no a commercial entity that has developed a top-level weather model which allows researchers a view of its methods – unlike nearly all systems which are offered free to the general audience in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.