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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa reaching a Category 5 hurricane. While I am not ready to forecast that strength yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The Model Works

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to generate an result, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for decades that can take hours to process and need the largest supercomputers in the world.

Expert Responses and Future Advances

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

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not just chance.”

He said that although Google DeepMind is outperforming all competing systems on predicting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he plans to talk with Google about how it can make the DeepMind output more useful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be really, really good, the output of the model is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its methods – unlike nearly all other models which are offered at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting AI to address challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.