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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am unprepared to predict that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer AI model dedicated to hurricanes, and currently the initial to beat standard meteorological experts at their specialty. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way The Model Works

Google’s model works by spotting patterns that traditional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an instance of machine learning – a technique that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can take hours to process and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that the AI could outperform earlier gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He said that while the AI is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can make the AI results even more helpful for forecasters by offering extra internal information they can use to assess the reasons it is producing its answers.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which allows researchers a view of its techniques – unlike most systems which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in adopting AI to address difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the national monitoring system.