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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am not ready to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform standard weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, possibly saving lives and property.

How The System Functions

The AI system works by identifying trends that conventional lengthy scientific prediction systems may overlook.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based 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 weather science for a long time – and is not generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Advances

Still, the fact that the AI could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin said that while the AI is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he plans to discuss with the company about how it can make the DeepMind output more useful for experts by providing additional internal information they can use to evaluate the reasons it is producing its answers.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Wider Sector Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its methods – in contrast to most systems which are provided at no cost 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 address challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.