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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a most intense storm. Although I am not ready to predict that intensity at this time given path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Across all tropical systems so far this year, the AI is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How Google’s System Functions

Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that authorities have used for decades that can require many hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that Google’s model could outperform earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

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

Franklin said that while Google DeepMind is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts 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 stated he intends to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

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

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its methods – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.