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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

As the lead forecaster 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. No forecaster had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. While I am not ready to forecast that strength yet due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system focused on tropical cyclones, and currently the first to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the disaster, potentially preserving people and assets.

The Way Google’s System Functions

The AI system works by identifying trends that conventional lengthy physics-based weather models may miss.

“The AI performs much more quickly than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

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

Understanding AI Technology

To be sure, the system is an instance of AI training – a technique that has been used in data-heavy sciences like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can require many hours to run and require the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that the AI could exceed previous gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of beginner’s luck.”

Franklin noted that while the AI is outperforming all competing systems on predicting the future path of storms globally this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he intends to talk with Google about how it can enhance the AI results even more helpful for experts by offering 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 although these predictions appear highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – unlike nearly all other models which are offered free to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The authorities also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.