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

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense storm. While I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the storm drifts over very warm ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the initial to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms this season, the AI is top-performing – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the disaster, potentially preserving lives and property.

The Way Google’s Model Functions

The AI system works by spotting patterns 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 less expensive and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the primary systems that governments have used for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the reality that the AI could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that although Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can make the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts appear really, really good, the output of the system is kind of a black box,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its techniques – unlike nearly all other models which are provided free to the general audience in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The US and European governments also have their respective AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.