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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

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

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 storm. Although I am not ready to predict that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system moves slowly 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 dedicated to tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

How Google’s System Works

Google’s model operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to process and require the largest supercomputers in the world.

Expert Responses and Upcoming Developments

Still, the fact that Google’s model could outperform previous gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

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

He noted that although the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can enhance the AI results more useful for forecasters by providing additional internal information they can utilize to assess exactly why it is producing its conclusions.

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

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – in contrast to most systems which are offered at no cost to the general audience in their entirety by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over previous traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.