🔗 Share this article

When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. While I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that conventional time-intensive scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can take hours to process and need the largest supercomputers in the world.

Expert Responses and Future Developments

Still, the reality that the AI could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

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

He said that while Google DeepMind is beating all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can make the AI results even more helpful for experts by offering additional internal information they can use to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a view of its methods – in contrast to most other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to solve difficult weather forecasting problems. The authorities also have their own artificial intelligence systems in the development phase – which have demonstrated 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 sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.