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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters 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 primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that strength at this time given track uncertainty, 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 is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving lives and property.

How Google’s Model Functions

Google’s model works by identifying trends that traditional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and need the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

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

He noted that although Google DeepMind is beating all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the DeepMind output even more helpful for experts by providing extra internal information they can utilize to assess exactly why it is producing its answers.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – in contrast to most other models which are offered free to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.