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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model 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 due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the system drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on tropical cyclones, and now the initial to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, potentially preserving people and assets.

How The Model Works

The AI system works by spotting patterns that traditional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“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 traditional weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that Google’s model could outperform earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can enhance the DeepMind output more useful for experts by providing extra internal information they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Broader Sector Developments

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its techniques – unlike most other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

The company is not alone in starting to use AI to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.