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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. 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 ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on hurricanes, and currently the first to beat standard weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Works

Google’s model operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a method that has been used in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a manner that its system only takes a few minutes to generate an answer, and can do so on a standard PC – in strong contrast to the primary systems that governments have used for years that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

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

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

During the next break, Franklin said he plans to discuss with the company about how it can make the DeepMind output more useful for forecasters by offering additional under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that while these forecasts appear highly accurate, the results of the system is essentially a black box,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its techniques – in contrast to most systems which are offered free to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in starting to use artificial intelligence to solve challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the national monitoring system.