🔗 Share this article

As Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident 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 first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on 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 AI ensemble members indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the initial to outperform standard meteorological experts at their own game. Across all tropical systems this season, the AI is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

How Google’s System Works

Google’s model operates through spotting patterns that conventional lengthy scientific weather models may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” he added.

Understanding AI Technology

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

AI training processes mounds of data and extracts trends from them in a manner that its model only requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that Google’s model could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

He said that while the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the AI results more useful for forecasters by providing additional internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its methods – in contrast to nearly all systems which are provided free to the public in their entirety by the governments 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 also have their own AI weather models in the works – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.