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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster 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 recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am not ready to forecast that intensity at this time due to path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the storm drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the first AI model dedicated to 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 the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, potentially preserving people and assets.

How Google’s Model Functions

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Clarifying Machine Learning

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

Machine learning takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Still, the reality that Google’s model could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

Franklin noted that while the AI is beating all competing systems on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, Franklin said he plans to talk with the company about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its answers.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the results of the model is essentially a black box,” said Franklin.

Wider Sector Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a view of its techniques – in contrast to most systems which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.