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As Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a most intense storm. Although I am unprepared to forecast that intensity yet given path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and now the first to outperform standard weather forecasters at their specialty. Across all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave residents additional preparation time to get ready for the disaster, possibly saving people and assets.

How The System Functions

Google’s model works by identifying trends that traditional lengthy scientific weather models may miss.

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

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to process and require some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, 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 predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while the AI is beating all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can make the AI results even more helpful for experts by offering additional under-the-hood data they can use to assess the reasons it is producing its answers.

“A key concern that troubles me is that while these predictions appear highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – unlike most other models which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not alone in adopting AI to address difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.