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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that strength yet due to track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the system drifts over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on tropical cyclones, and now the first to outperform traditional weather forecasters at their own game. Across all tropical systems this season, Google’s model is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided residents extra time to get ready for the catastrophe, potentially preserving people and assets.

How The System Functions

The AI system works by spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” Lowry added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to come up with an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have utilized for decades that can take hours to process and require the largest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the fact that the AI could outperform earlier gold-standard legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

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

He noted that although Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he plans to talk with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that although these predictions appear really, really good, the results of the system is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are provided at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.