🔗 Share this article

When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, 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 people and assets.

How Google’s System Works

The AI system operates through spotting patterns that conventional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that governments have utilized for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by providing additional under-the-hood data they can utilize to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to nearly all systems which are provided free to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems 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 difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.