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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI 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 key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system drifts over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to outperform standard meteorological experts at their own game. Across all tropical systems so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction likely gave residents extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The Model Functions

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

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that the AI could exceed previous top-tier traditional systems 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 expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although the AI is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering additional internal information they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a peek into its techniques – unlike nearly all systems which are offered 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 address difficult meteorological problems. The authorities also have their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.