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

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the catastrophe, potentially preserving people and assets.

How The System Works

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

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner 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 authorities have used for decades that can take hours to run and require the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that although the AI is outperforming all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can enhance the AI results more useful for forecasters by providing additional under-the-hood data they can use to assess the reasons it is coming up with its answers.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the system is kind of a opaque process,” remarked Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to nearly all systems which are provided free to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the national monitoring system.