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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. While I am not ready to predict that intensity yet due to path variability, that is still plausible.

“There is a high probability that a period of rapid intensification 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 Conventional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the first to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction likely gave residents extra time to prepare for the disaster, potentially preserving lives and property.

How The System Works

The AI system works by identifying trends that conventional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events 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 traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that authorities have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the fact that the AI could outperform earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not a case of chance.”

Franklin said that although the AI is beating all other models on forecasting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he stated he intends to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional internal information they can utilize to assess the reasons it is producing its conclusions.

“A key concern that troubles me is that although these predictions seem to be really, really good, the results of the model is kind of a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to most systems which are provided at no cost to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to fill the gaps in the national monitoring system.