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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the first to beat traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

The Way The System Functions

Google’s model works by identifying trends that traditional lengthy scientific weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events 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 forecasting tools we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to run and require the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the fact that Google’s model could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by providing extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“A key concern that troubles me is that although these predictions appear highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its methods – unlike most other models which are provided free to the public in their entirety by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.