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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, possibly saving people and assets.

How The Model Functions

Google’s model operates through identifying trends that conventional lengthy scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate 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 method that has been used in data-heavy sciences like meteorology for a long time – and is not 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 requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for decades that can take hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that the AI could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just chance.”

He said that while the AI is outperforming all other models on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“The one thing that troubles me is that while these predictions appear highly accurate, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its methods – in contrast to nearly all other models which are offered at no cost to the general audience in their full form by the governments that created and operate them.

Google is not alone in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.