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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and now the first to outperform standard weather forecasters at their own game. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Functions

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes 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 do so on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that the AI could exceed earlier top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

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

Franklin said that while the AI is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he intends to discuss with the company 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 predictions seem to be really, really good, the output of the model is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – unlike nearly all other models which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.