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As Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense storm. While I am unprepared to forecast that intensity at this time due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the system drifts over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model dedicated to hurricanes, and currently the first to outperform traditional weather forecasters at their own game. Across all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How Google’s Model Works

Google’s model operates through identifying trends that conventional time-intensive scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry added.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for years that can require many hours to run and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Still, the reality that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although the AI is outperforming all other models on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, he said he intends to talk with the company about how it can enhance the AI results even more helpful for forecasters by offering 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 predictions appear highly accurate, the output of the system is kind of a black box,” said Franklin.

Broader Sector Trends

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – unlike nearly all other models which are offered free to the public in their entirety by the authorities that designed and maintain them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.