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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on AI Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to predict that intensity at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the storm drifts over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the initial to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, possibly saving lives and property.

The Way The System Functions

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” Lowry said.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the reality that the AI could outperform previous top-tier legacy models so rapidly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest weather systems.

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

Franklin noted that while Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can utilize to assess the reasons it is producing its answers.

“A key concern that nags at me is that although these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a top-level forecasting system which allows researchers a peek into its techniques – in contrast to nearly all other models which are provided at no cost to the public in their entirety by the authorities that created and operate them.

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

Future developments in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.