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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 hurricane. Although I am not ready to forecast that strength yet given path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the initial to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The Model Functions

The AI system works by spotting patterns that conventional time-intensive physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid traditional forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

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

AI training takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can utilize to evaluate the reasons it is producing its answers.

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

Wider Industry 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 systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not the only one in adopting AI to solve difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.