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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity at this time given path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system focused on hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems this season, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the catastrophe, potentially preserving lives and property.

How Google’s System Works

Google’s model works by spotting patterns that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends 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 strong contrast to the primary systems that governments have utilized for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

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

Franklin said that while Google DeepMind is outperforming all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he plans to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions appear really, really good, the output of the system is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a peek into its methods – unlike most other models which are provided at no cost to the general audience in their full form by the authorities that designed and maintain them.

Google is not alone in adopting AI to solve difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.