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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence 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 AI simulation runs show Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first AI model focused on hurricanes, and now the initial to outperform standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s Model Functions

The AI system works by identifying trends that conventional time-intensive scientific weather models may miss.

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

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the reality that the AI could outperform previous top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

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

During the next break, Franklin stated he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by providing additional internal information they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that although these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – unlike nearly all systems which are offered free to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in starting to use AI to solve difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the national monitoring system.