🔗 Share this article

When Developing Cyclone Melissa swirled south of Haiti, weather expert 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 weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

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

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 storm. Although I am not ready to predict that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer AI model dedicated to hurricanes, and currently the first to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction probably provided residents extra time to prepare for the disaster, potentially preserving lives and property.

The Way The Model Functions

Google’s model works by identifying trends that traditional lengthy physics-based 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 former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to generate an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for years that can take hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could outperform previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired expert. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin noted that although Google DeepMind is outperforming all other models on forecasting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can make the AI results more useful for forecasters by offering extra under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that troubles me is that while these predictions appear highly accurate, the results of the model is essentially a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are provided free to the public in their full form by the governments that created and operate them.

The company is not alone in starting to use AI to solve challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.