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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that strength at this time due to path variability, that remains a possibility.

“It appears likely that a period of quick strengthening will occur as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and now the initial to beat standard meteorological experts at their specialty. Through all tropical systems this season, 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 coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided residents extra time to get ready for the disaster, possibly saving people and assets.

The Way The System Functions

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of AI training – a method that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for years that can require many hours to run and require the largest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the fact that the AI could outperform previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

He noted that although the AI is beating all competing systems on predicting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by providing extra internal information they can utilize to assess the reasons it is producing its answers.

“The one thing that nags at me is that while these forecasts seem to be highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its methods – in contrast to most other models which are provided at no cost to the public in their full form by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.