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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted 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. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength at this time given path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the first to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, possibly saving lives and property.

The Way Google’s Model Works

Google’s model works by spotting patterns that traditional time-intensive scientific prediction systems may miss.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” 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 weather science for a long time – and is distinct from generative AI 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 generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can require many hours to process and need the largest supercomputers in the world.

Professional Responses and Future Developments

Nevertheless, the fact that the AI could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

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

Franklin said that while the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he intends to talk with the company about how it can make the AI results more useful for forecasters by providing additional internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.