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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to predict that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform traditional meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to get ready for the disaster, possibly saving people and assets.

How The System Works

Google’s model works by identifying trends that traditional lengthy scientific prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a method that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he said he intends to talk with the company about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can utilize to evaluate the reasons it is producing its answers.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the system is essentially a black box,” said Franklin.

Wider Industry Developments

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – in contrast to most systems which are provided at no cost to the general audience in their full form by the governments that created and operate them.

The company is not alone in starting to use AI to address challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.