🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that strength at this time given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model dedicated to hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Through all tropical systems this season, Google’s model is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

How Google’s Model Functions

The AI system works by identifying trends that traditional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a technique that has been employed in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for years that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not a case of chance.”

Franklin said that while the AI is beating all competing systems on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the DeepMind output more useful for experts by providing additional under-the-hood data they can use to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that although these forecasts seem to be highly accurate, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – in contrast to nearly all systems which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult meteorological problems. The authorities also have their respective AI weather models in the works – which have also shown better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.