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As Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, 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. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense hurricane. Although I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the system moves slowly over exceptionally hot ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the first AI model dedicated to hurricanes, and currently the first to beat standard meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s System 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 physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been employed in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and require some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

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

He noted that while Google DeepMind is beating all other models on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by offering additional internal information they can utilize to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these predictions appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – unlike most systems which are provided at no cost to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.