The Way Alphabet’s AI Research System is Revolutionizing Tropical Cyclone Forecasting with Rapid Pace

As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense storm. While I am unprepared to forecast that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm moves slowly over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer 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, Google’s model is top-performing – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way The Model Functions

Google’s model works by identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that the AI could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although the AI is outperforming all other models on forecasting the future path 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 rapid intensification to category 5 north of the Caribbean.

During the next break, he said he intends to discuss with Google about how it can make the AI results even more helpful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is producing its conclusions.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Trends

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

Google is not alone in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.