🔗 Share this article

As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted 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 previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and now the first to beat traditional meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The Model Functions

The AI system operates through spotting patterns that traditional time-intensive scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that although the AI is beating all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can make the AI results more useful for experts by providing additional under-the-hood data they can use to evaluate 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 output of the model is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all other models which are provided free to the public in their full form by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.