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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI simulation runs show Melissa becoming a most intense storm. While I am not ready to predict that strength at this time given path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Across all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Functions

Google’s model works by identifying trends that conventional time-intensive scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can take hours to run and need the largest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that Google’s model could exceed previous gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that although the AI is outperforming all competing systems on forecasting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that while these predictions seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its techniques – in contrast to most other models which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to address difficult meteorological problems. The US and European governments also have their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.