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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for quick intensification.

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. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 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 reaching a Category 5 storm. While I am unprepared to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the first to outperform standard meteorological experts at their own game. Through all tropical systems so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

How The System Works

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

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

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind 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 mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest weather systems.

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

Franklin noted that while Google DeepMind is outperforming all competing systems on predicting the future path of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the AI results even more helpful for forecasters by providing additional internal information they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Industry Developments

Historically, no a commercial entity that has developed a high-performance weather model which allows researchers a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their entirety by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.