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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced 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.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. While I am unprepared to predict that strength yet given path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system moves slowly over very warm ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving people and assets.

How The System Functions

The AI system works by spotting patterns that conventional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a method 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 system only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for decades that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the fact that the AI could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin said that while Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he said he intends to talk with Google about how it can make the DeepMind output even more helpful for experts by offering extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

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

Wider Sector Developments

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a view of its methods – unlike most systems which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The authorities also have their respective AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.