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When Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

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

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 storm. Although I am unprepared to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

The Way Google’s System Works

Google’s model works by identifying trends that traditional lengthy physics-based prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” he added.

Clarifying AI Technology

It’s important to note, the system is an example of AI training – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have utilized for years that can require many hours to run and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

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

He said that although Google DeepMind is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength predictions wrong. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can make the DeepMind output more useful for forecasters by providing extra internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

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

Broader Industry Trends

There has never been a private, for-profit company that has produced a top-level weather model which grants experts a view of its techniques – unlike nearly all other models which are offered at no cost to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.