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

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat traditional meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Works

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

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

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can take hours to run and require the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Still, the fact that Google’s model could outperform earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

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

He noted that while the AI is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he intends to talk with Google about how it can enhance the AI results even more helpful for experts by providing extra internal information they can utilize to assess exactly why it is producing its conclusions.

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

Wider Industry Developments

Historically, no a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its techniques – unlike most systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the national monitoring system.