🔗 Share this article

As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 storm. While I am not ready to predict that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to outperform standard meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by identifying trends that conventional lengthy physics-based weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been used in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for years that can take hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the fact that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

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

He said that although Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to discuss with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering extra internal information they can utilize to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the system is essentially a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – in contrast to most systems which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.