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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the storm drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the initial to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How The Model Works

The AI system works by identifying trends that conventional time-intensive scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model 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 utilized for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Still, the reality that the AI could outperform previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s evident this is not just chance.”

He noted that although Google DeepMind is beating all competing systems on predicting the trajectory of storms globally this year, similar to other systems it occasionally gets extreme strength predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the AI results even more helpful for experts by offering extra under-the-hood data they can utilize to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – in contrast to most systems which are provided free to the public in their full form by the governments that created and operate them.

The company 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 also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.