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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the lead forecaster on duty, he forecasted 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. Not a single expert had ever issued such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am not ready to predict that strength at this time given path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification is expected as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to outperform traditional meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How Google’s System Works

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

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been used in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for years that can require many hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that the AI could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

Franklin noted that although the AI is beating all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to talk with the company about how it can enhance the AI results even more helpful for experts by providing extra internal information they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that nags at me is that while these forecasts seem to be really, really good, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a top-level weather model which grants experts a view of its methods – in contrast to nearly all other models which are provided free to the general audience in their entirety by the authorities that created and operate them.

Google is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.