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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense storm. Although I am not ready to predict that strength at this time given path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How The Model Functions

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

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

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not just chance.”

Franklin noted that while the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he intends to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing additional under-the-hood data they can utilize to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions seem to be really, really good, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its techniques – in contrast to nearly all systems which are offered free to the public in their full form by the governments that created and operate them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.