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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the storm would become a severe 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.

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

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a most intense storm. Although I am unprepared to predict that intensity at this time given path variability, that remains a possibility.

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

Surpassing 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 own game. Across all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How Google’s Model Functions

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

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

“This season’s events has proven in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid traditional weather models 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 employed in data-heavy sciences like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have used for decades that can require many hours to run and require the largest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the fact that the AI could outperform earlier top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense storms.

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

Franklin noted that although the AI is beating all competing systems on forecasting the trajectory of storms globally this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with another storm previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the AI results even more helpful for experts by offering extra internal information they can utilize to evaluate the reasons it is producing its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its methods – unlike nearly all systems which are provided at no cost to the public in their full form by the authorities that created and operate them.

The company is not the only one in starting to use AI to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the US weather-observing network.