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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system 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 forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form 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 tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense storm. Although I am unprepared to predict that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm drifts over very warm sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

How Google’s Model Works

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an instance of AI training – a technique that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for years that can take hours to process and require the largest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all other models on predicting the trajectory 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 also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he plans to talk with the company about how it can enhance the AI results even more helpful for experts by providing extra internal information they can use to evaluate exactly why it is coming up with its conclusions.

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

Broader Industry Trends

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in starting to use AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.