How Google’s AI Research System is Transforming Hurricane Forecasting with Speed

As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the storm would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to predict that intensity yet due to path variability, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to outperform traditional weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Functions

The AI system works by spotting patterns that traditional lengthy physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

To be sure, Google DeepMind is an example of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an answer, and can do so on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can take hours to process and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed previous gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just chance.”

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he intends to talk with the company about how it can make the DeepMind output more useful for experts by providing additional internal information they can use to evaluate the reasons it is producing its conclusions.

“The one thing that nags at me is that although these predictions seem to be really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a view of its methods – unlike nearly all systems which are offered free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured federal support to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.