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As Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’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 Dependence on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. 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 ensemble members show Melissa becoming a most intense hurricane. Although I am not ready to predict that intensity yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the disaster, potentially preserving lives and property.

How Google’s Model Functions

Google’s model operates through identifying trends that conventional lengthy scientific weather models may overlook.

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

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” he said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have utilized for decades that can take hours to process and require some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “The sample is now large enough that it’s evident this is not a case of chance.”

He noted that although the AI is beating all other models on predicting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with Google about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has produced a high-performance weather model which grants experts a peek into its methods – unlike most systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the national monitoring system.