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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

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

Increasing Dependence 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 key factor for his confidence: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense storm. Although I am not ready to forecast that strength at this time due to path variability, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Works

The AI system operates through spotting patterns that traditional time-intensive scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to generate an answer, and can do so on a standard PC – in sharp difference to the primary systems that authorities have used for years that can require many hours to process and require the largest supercomputers in the world.

Professional Reactions and Future Developments

Nevertheless, the reality that the AI could exceed previous gold-standard traditional systems so quickly 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 former expert. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can make the AI results more useful for forecasters by offering additional under-the-hood data they can use to assess the reasons it is producing its answers.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to most systems which are provided free to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in starting to use artificial intelligence to address difficult meteorological problems. The authorities also have their respective AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the national monitoring system.