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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system 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.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. While I am not ready to predict that strength yet given path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. The confident prediction likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

How Google’s System Functions

Google’s model works by spotting patterns that conventional lengthy scientific weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a method that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training 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 strong contrast to the flagship models that governments have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

He noted that although Google DeepMind is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is producing its conclusions.

“A key concern that troubles me is that while these predictions seem to be highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in starting to use AI to address challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.