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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

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 towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to predict that intensity at this time due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents extra time to prepare for the disaster, possibly saving lives and property.

How The Model Functions

Google’s model operates through spotting patterns that traditional lengthy scientific weather models may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, the system is an example of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can take hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the fact that the AI could exceed previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense storms.

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

Franklin said that while Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that nags at me is that while these forecasts appear really, really good, the results of the system is essentially a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its techniques – in contrast to nearly all systems which are offered at no cost to the public in their full form by the governments that designed and maintain them.

Google is not the only one in adopting AI to solve difficult weather forecasting problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.