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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 storm. Although I am unprepared to forecast 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 system moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer artificial intelligence system focused on tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the disaster, potentially preserving lives and property.

How The Model Works

Google’s model works by identifying trends that traditional time-intensive physics-based weather models may miss.

“The AI performs much more quickly than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – 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 processes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Advances

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

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

He said that while Google DeepMind is beating all other models on predicting the future path of hurricanes globally 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 category 5 above the Caribbean.

During the next break, Franklin stated he intends to discuss with the company about how it can enhance the AI results even more helpful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“A key concern that nags at me is that while these forecasts appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its methods – unlike most systems which are offered at no cost to the general audience in their entirety by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to address challenging weather forecasting problems. The authorities 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 AI weather forecasts seem to be new firms tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the national monitoring system.