🔗 Share this article

As Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that intensity yet given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The Model Functions

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

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said 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 exactly why it is producing its conclusions.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all other models which are offered free to the general audience in their entirety by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve 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 have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.