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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence 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 did become a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am unprepared to predict that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period 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 Traditional Models

Google DeepMind is the first AI model focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way The Model Functions

Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, 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 newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of machine learning – a method that has been used in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have used for years that can take hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a former expert. “The sample is now large enough that it’s pretty clear this is not just beginner’s luck.”

He said that while the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with Google about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can use to assess exactly why it is producing its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the results of the system is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a peek into its methods – unlike most other models which are offered at no cost to the public in their full form by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to solve difficult meteorological problems. The authorities are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.