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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that intensity at this time given track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their own game. Through all tropical systems so far this year, Google’s model is the best – even beating experts on track predictions.

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

The Way Google’s System Functions

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

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

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for years that can take hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the reality that Google’s model could outperform previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin said that while the AI is outperforming all other models on predicting the future path of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can make the AI results even more helpful for experts by offering extra under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear really, really good, the results of the system is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – unlike nearly all systems which are provided at no cost to the general audience in their full form by the authorities that created and operate them.

Google is not alone in starting to use AI to solve challenging weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.