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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the storm would become 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 forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa becoming a most intense hurricane. While I am unprepared to predict that strength at this time due to path variability, that is still plausible.

“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 highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

Google’s model works by identifying trends that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in research fields like meteorology for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that Google’s model could exceed previous top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” said 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.”

He noted that while the AI is outperforming all competing systems on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by providing extra internal information they can use to evaluate exactly why it is coming up with its answers.

“The one thing that troubles me is that although these predictions seem to be really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all systems which are provided at no cost to the public in their full form by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.