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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. Although I am unprepared to forecast that intensity yet given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, possibly saving lives and property.

How Google’s Model Works

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been used in research fields like weather science 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 model only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Future Developments

Still, the reality that Google’s model could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

Franklin noted that while the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he plans to talk with the company about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“The one thing that nags at me is that although these predictions seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has developed 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 the only one in starting to use AI to address difficult meteorological problems. The US and European governments also have their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the US weather-observing network.