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

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. Although I am not ready to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over very warm ocean waters which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Through all tropical systems this season, the AI is top-performing – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, potentially preserving lives and property.

How Google’s System Works

Google’s model operates through identifying trends that conventional lengthy scientific prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not just beginner’s luck.”

He noted that while the AI is outperforming all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above 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 extra under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that while these forecasts appear highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into 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.

Google is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.