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

As the primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am not ready to forecast that strength yet given path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over very warm ocean waters which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

How The Model Works

Google’s model works by spotting patterns that traditional lengthy physics-based weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

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

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for years that can require many hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The sample is now large enough 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, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts seem to be highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to nearly all other models which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.