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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and now the first to outperform standard weather forecasters at their specialty. Through all tropical systems this season, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the disaster, possibly saving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that traditional lengthy physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have utilized for years that can take hours to run and need the largest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts 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 said he intends to talk with the company about how it can enhance the AI results more useful for experts by providing additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

“The one thing that troubles me is that although these forecasts appear highly accurate, the results of the system is kind of a black box,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its methods – in contrast to nearly all other models which are provided free to the general audience in their entirety by the governments that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.