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

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe 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 possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Forecasting

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 certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense storm. While I am unprepared to predict that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and now the initial to outperform standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model operates through spotting patterns that traditional lengthy scientific weather models may miss.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets 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 authorities have used for years that can take hours to process and need the largest supercomputers in the world.

Expert Responses and Future Advances

Still, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just beginner’s luck.”

He noted that while Google DeepMind is outperforming all other models on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can make the AI results even more helpful for experts by offering extra 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 while these forecasts appear really, really good, the results of the system is essentially a opaque process,” said Franklin.

Wider Sector Developments

Historically, no a commercial entity that has produced a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are provided free 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 solve challenging weather forecasting problems. The authorities are developing their own AI weather models in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.