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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on 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 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. While I am not ready to predict that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way The Model Works

The AI system works by identifying trends that traditional lengthy physics-based weather models may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an result, and can operate on a standard PC – in strong contrast to the primary systems that authorities have used for years that can require many hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Nevertheless, the fact that Google’s model could exceed earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can make the DeepMind output even more helpful for experts by offering additional internal information they can use to evaluate the reasons it is coming up with its answers.

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

Wider Sector Trends

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a view of its methods – unlike most other models which are offered 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 demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks 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 address deficiencies in the US weather-observing network.