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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense storm. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way Google’s System Works

The AI system works by identifying trends that conventional 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 time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that while the AI is beating all competing systems on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can make the AI results even more helpful for experts by providing extra under-the-hood data they can utilize to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the output of the system is essentially a black box,” said Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a peek into its methods – in contrast to most other models which are offered at no cost to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.