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As Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. 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 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. Although I am not ready to predict that strength at this time given path variability, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system moves slowly over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way Google’s Model Works

Google’s model works by spotting patterns that conventional time-intensive physics-based weather models may overlook.

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

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can make the AI results even more helpful for experts by offering extra internal information they can use to evaluate the reasons 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 black box,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its methods – in contrast to most other models which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.