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When Tropical Storm 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 intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 storm. Although I am unprepared to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first AI model focused on hurricanes, and now the initial to outperform traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is the best – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Functions

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

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in research fields like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that although Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can make the AI results even more helpful for experts by providing extra internal information they can use to evaluate exactly why it is producing its conclusions.

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

Broader Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – unlike most other models which are offered free to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved 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 atmospheric sensors to address deficiencies in the national monitoring system.