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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm 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 had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to predict that strength yet given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over very warm sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Works

The AI system operates through identifying trends that traditional time-intensive scientific weather models may overlook.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, 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 mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require the largest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

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

Franklin said that while the AI is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he stated he plans to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional internal information they can utilize to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the output of the system is kind of a black box,” said Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a high-performance weather model which allows researchers a peek into its methods – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the authorities that designed and maintain them.

Google is not the only one in adopting AI to solve difficult meteorological problems. The authorities are developing their respective AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the US weather-observing network.