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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am not ready to forecast that strength at this time due to path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

How Google’s System Functions

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

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, superior than the slower physics-based forecasting tools we’ve relied upon,” Lowry said.

Understanding AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to generate an result, and can do so on a standard PC – in sharp difference to the flagship models that governments have used for decades that can require many hours to process and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Still, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

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

Franklin noted that while the AI is outperforming all competing systems on predicting the future path of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by offering extra under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts appear highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a view of its techniques – in contrast to most other models which are provided at no cost to the public in their entirety by the governments that created and operate them.

The company is not the only one in adopting AI to address challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.