🔗 Share this article

When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to forecast that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is top-performing – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction probably provided residents extra time to get ready for the disaster, possibly saving lives and property.

The Way The System Functions

The AI system works by spotting patterns that conventional time-intensive physics-based weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

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

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the reality that the AI could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

He noted that although the AI is outperforming all competing systems on predicting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can make the DeepMind output more useful for experts by providing extra internal information they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that although these forecasts seem to be really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a view of its methods – in contrast to most other models which are provided at no cost to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.