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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system focused on hurricanes, and now the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. The confident prediction probably provided residents extra time to get ready for the disaster, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex 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, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an instance of machine learning – a method that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to process and require the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the reality that Google’s model could exceed earlier gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

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

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty 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 talk with Google about how it can enhance the AI results more useful for forecasters by providing extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the model is essentially a black box,” remarked Franklin.

Wider Sector Trends

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

The company is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.