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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. While I am not ready to forecast that strength yet given path variability, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system drifts over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first artificial intelligence system focused on hurricanes, and now the initial to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How The Model Works

Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

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

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been used in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and require the largest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the reality that Google’s model could exceed previous gold-standard traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

He said that while 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 forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results more useful for forecasters by offering extra under-the-hood data they can use to assess the reasons it is producing its answers.

“The one thing that nags at me is that while these forecasts seem to be highly accurate, the results of the model is essentially a black box,” said Franklin.

Wider Industry Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a view of its techniques – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better 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 weather balloons to fill the gaps in the national monitoring system.