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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours 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 quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. Although I am unprepared to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform standard meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The Model Functions

Google’s model operates through spotting patterns that conventional lengthy scientific prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an result, 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 process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

He noted that although Google DeepMind is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

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

“A key concern that troubles me is that while these predictions appear highly accurate, the output of the model is essentially a black box,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – in contrast to most systems which are provided free to the public in their full form by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to address challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.