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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon 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 severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to predict that intensity yet given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way The Model Functions

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” he said.

Understanding Machine Learning

It’s important to note, the system is an example of machine learning – a method that has been employed in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that Google’s model could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while Google DeepMind is beating all competing systems on predicting 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 also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by offering additional internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions seem to be really, really good, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its techniques – in contrast to most systems which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – 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 improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the national monitoring system.