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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. 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 the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists 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 primary reason for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

How Google’s System Functions

The AI system operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been employed in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning 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 sharp difference to the flagship models that governments have used for decades that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while the AI is beating all competing systems on predicting the future path of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can enhance the AI results even more helpful for experts by providing extra internal information they can utilize to evaluate exactly why it is producing its conclusions.

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

Broader Sector Trends

Historically, no a commercial entity that has developed a top-level forecasting system which grants experts a view of its techniques – unlike nearly all systems which are provided free to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting AI to address difficult weather forecasting problems. The authorities also have their own AI weather models in the works – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.