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When Tropical Storm Melissa was churning 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 forecasted that in a single day the storm would intensify into a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. While I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform traditional weather forecasters at their specialty. Through all tropical systems so far this year, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 strength, one of the strongest landfalls ever documented in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way Google’s System Works

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

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

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 generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that Google’s model could exceed earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest 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 a case of chance.”

Franklin said that while the AI is beating all competing systems on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can enhance the AI results more useful for forecasters by offering additional under-the-hood data they can use to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that although these predictions appear highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which allows researchers 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 created and operate them.

The company is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.