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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that intensity yet due to path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

How The Model Works

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

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a method that has been used in research fields like weather science for a long time – and is not generative AI 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 answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Still, the fact that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin noted that while the AI is beating all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results even more helpful for forecasters by offering extra internal information they can utilize to evaluate exactly why it is producing its answers.

“A key concern that nags at me is that while these predictions seem to be highly accurate, 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 top-level forecasting system which allows researchers a view of its techniques – unlike most systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.