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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a Category 5 storm. Although I am unprepared to predict that strength yet due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to beat standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the disaster, potentially preserving lives and property.

The Way The Model Works

The AI system works by spotting patterns that conventional lengthy physics-based weather models may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for decades that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

Franklin said that although Google DeepMind is outperforming all other models on forecasting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to talk with Google about how it can make the AI results more useful for experts by providing additional under-the-hood data they can use to assess exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

Google is not alone in starting to use AI to solve difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.