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As 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.

Serving as lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – launched for the first time in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am not ready to predict that intensity at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening will occur as the system drifts over exceptionally hot 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 focused on tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Through all tropical systems this season, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the disaster, possibly saving people and assets.

The Way The System Functions

Google’s model operates through spotting patterns that conventional lengthy physics-based prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional 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 a long time – and is not creative artificial intelligence like ChatGPT.

AI training takes 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 operate on a desktop computer – in sharp difference to the primary systems that governments have used for decades that can take hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could exceed previous top-tier legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense weather systems.

“I’m impressed,” said James Franklin, a former expert. “The data is sufficient that it’s pretty clear this is not a case of chance.”

He noted that although the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he stated he intends to talk with Google about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can use to assess exactly why it is producing its conclusions.

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

Broader Sector Developments

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to most systems which are provided free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not alone in adopting AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.