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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that intensity at this time given path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model works by identifying trends that traditional lengthy scientific weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in certain instances, more accurate than the less rapid traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, the system is an example of machine learning – a technique that has been used in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes 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 result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Upcoming Advances

Still, the reality that the AI could exceed earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a retired expert. “The data is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that while the AI is beating all competing systems on forecasting the future path of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can enhance the AI results even more helpful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

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

Wider Industry Developments

There has never been a private, for-profit company that has developed a high-performance forecasting system which allows researchers a view of its methods – in contrast to nearly all systems which are offered free to the public in their entirety by the governments that created and operate them.

Google is not alone in adopting artificial intelligence to address difficult meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously 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 deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.