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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 forecasted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first AI model dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, potentially preserving lives and property.

The Way The System Works

Google’s model operates through identifying trends that traditional time-intensive physics-based prediction systems may miss.

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

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that governments have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Still, the reality that the AI could outperform earlier gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the results of the model is essentially a opaque process,” said Franklin.

Broader Industry Trends

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a view of its methods – in contrast to nearly all other models which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in starting to use AI to address difficult meteorological problems. The authorities are developing their respective AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.