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When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am unprepared to forecast that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model dedicated to hurricanes, and now the initial to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms 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, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, potentially preserving lives and property.

How Google’s System Functions

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for decades that can require many hours to process and need the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the reality that the AI could exceed previous gold-standard traditional systems 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,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just beginner’s luck.”

Franklin said that while the AI is outperforming all competing systems on predicting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can make the AI results even more helpful for experts by offering additional under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the model is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level weather model which allows researchers a view of its methods – in contrast to nearly all systems which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.