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As Developing Cyclone Melissa swirled south of Haiti, weather expert 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 become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon 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: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 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 is expected as the system drifts over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the initial to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

How The Model Functions

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

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“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.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

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

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s evident this is not just chance.”

Franklin noted that although Google DeepMind is beating all other models on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing extra internal information they can use to evaluate exactly why it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – in contrast to nearly all other models which are offered at no cost to the public in their full form by the authorities that created and operate them.

Google is not the only one in starting to use AI to address difficult meteorological problems. The authorities also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.