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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. 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 reaching a most intense storm. Although I am not ready to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their specialty. Across all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

How Google’s System Functions

Google’s model operates through identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive 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 on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

It’s important to note, the system is an instance of machine learning – a method that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest 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 a case of chance.”

He said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he stated he plans to discuss with the company about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can use to evaluate the reasons it is coming up with its conclusions.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Wider Sector Trends

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to nearly all other models which are offered free to the public in their entirety by the governments that designed and maintain them.

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

Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.