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As Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, 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 catastrophe, potentially preserving lives and property.

The Way Google’s System Works

The AI system works by spotting patterns that traditional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

To be sure, the system is an instance of AI training – a technique that has been employed in data-heavy sciences 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 system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could exceed previous 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.

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

He said that while Google DeepMind is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he intends to talk with the company about how it can make the AI results even more helpful for forecasters by providing extra internal information they can use to evaluate the reasons it is producing its answers.

“The one thing that nags at me is that although these predictions appear really, really good, the output of the model is kind of a black box,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has produced a top-level weather model which allows researchers a peek into its techniques – in contrast to nearly all other models which are offered at no cost to the general audience in their full form by the governments that created and operate them.

Google is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.