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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance 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 primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa reaching a Category 5 hurricane. While I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to beat standard weather forecasters at their own game. Across all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to get ready for the disaster, possibly saving people and assets.

The Way The Model Functions

The AI system works by spotting patterns that traditional lengthy scientific prediction systems may miss.

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

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Clarifying AI Technology

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

Machine learning processes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could outperform previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense weather systems.

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

He noted that while Google DeepMind is beating all other models on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by offering extra internal information they can use to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear really, really good, the output of the model is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its techniques – unlike nearly all systems which are offered free to the public in their full form by the authorities that designed and maintain them.

Google is not alone in starting to use AI to address challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.