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

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon Google DeepMind. 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 AI ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that strength yet given track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided residents additional preparation time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

Google’s model works by identifying trends that traditional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in research fields like meteorology for years – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

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

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

Franklin noted that while Google DeepMind is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can enhance the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts 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.

The company is not alone in starting to use AI to address challenging weather forecasting problems. The authorities also have their own artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.