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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model focused on tropical cyclones, and currently the first to beat traditional meteorological experts at their own game. Through all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum strength, one of the strongest landfalls recorded in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Functions

The AI system operates through spotting patterns that traditional lengthy scientific prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

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

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – 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 supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

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

Franklin said that while Google DeepMind is beating all other models on predicting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he plans to talk with Google about how it can make the AI results more useful for forecasters by offering additional internal information they can utilize to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that while these predictions appear highly accurate, the output of the system is kind of a black box,” said Franklin.

Wider Sector Trends

There has never been a private, for-profit company that has produced a high-performance weather model which grants experts a view of its techniques – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its own weather balloons to address deficiencies in the US weather-observing network.