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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am not ready to forecast that strength yet given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to tropical cyclones, and now the initial to beat standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The System Works

The AI system works by identifying trends that traditional time-intensive scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can require many hours to process and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with Google about how it can enhance the AI results more useful for experts by providing additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts appear highly accurate, the results of the system is kind of a black box,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to nearly all other models which are provided free to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.