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

Serving as lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a Category 5 storm. While I am not ready to predict that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model focused on tropical cyclones, and currently the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve relied upon,” he said.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a method that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have utilized for decades that can take hours to process and require the largest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

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

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing additional internal information they can use to assess exactly why it is producing its answers.

“A key concern that nags at me is that although these forecasts appear really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its methods – unlike most systems which are offered free to the general audience in their full form by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.