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

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. While I am not ready to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the storm moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the first to beat standard weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

How Google’s System Functions

Google’s model operates through spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems 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

To be sure, the system is an instance of machine learning – a method that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner 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 authorities have used for years that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could exceed earlier gold-standard traditional systems so quickly 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 former expert. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

He noted that although Google DeepMind is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can enhance the AI results more useful for forecasters by providing additional internal information they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions seem to be highly accurate, the output of the system is essentially a black box,” said Franklin.

Wider Sector Developments

Historically, no a commercial entity that has developed a top-level weather model which grants experts a view of its techniques – in contrast to nearly all other models which are offered at no cost to the public in their entirety by the authorities that created and operate them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.