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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense storm. While I am not ready to forecast that strength yet due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model dedicated to hurricanes, and currently the initial to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. The confident prediction probably provided residents additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way Google’s Model Functions

The AI system works by spotting patterns that conventional time-intensive scientific weather models may miss.

“The AI performs far faster 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 newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been employed in data-heavy sciences like weather science for a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can take hours to process and need the largest supercomputers in the world.

Professional Reactions and Future Developments

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

“It’s astonishing,” commented James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

He said that although the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these predictions appear really, really good, the results of the system is kind of a opaque process,” said Franklin.

Wider Sector Developments

Historically, no a private, for-profit company that has produced a high-performance forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are provided free to the public in their full form by the authorities that created and operate them.

The company is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms taking swings at formerly difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.