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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had ever issued this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense hurricane. Although I am unprepared to forecast that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening will occur as the storm drifts over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the first to outperform standard weather forecasters at their specialty. Through all tropical systems this season, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

The Way The System Functions

The AI system operates through spotting patterns that conventional lengthy physics-based weather models may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” he said.

Clarifying AI Technology

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

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for years that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Advances

Nevertheless, the reality that Google’s model could exceed previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not just chance.”

He noted that while the AI is beating all other models on forecasting the future path of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he intends to talk with the company about how it can make the DeepMind output more useful for forecasters by providing extra internal information they can utilize to evaluate the reasons it is producing its conclusions.

“The one thing that troubles me is that although these forecasts appear really, really good, the results of the system is essentially a opaque process,” said 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 techniques – unlike nearly all systems which are provided at no cost to the public in their full form by the governments that created and operate them.

The company is not the only one in adopting AI to solve difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of severe weather and flash flooding – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.