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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of Google’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.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 storm. While I am unprepared to predict that strength yet given path variability, that remains a possibility.

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

Outperforming Traditional Models

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s System Functions

Google’s model works by identifying trends that traditional time-intensive scientific prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are competitive with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference 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 Responses and Upcoming Advances

Nevertheless, the reality that Google’s model could outperform previous gold-standard traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he stated he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by providing extra under-the-hood data they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that while these predictions seem to be highly accurate, the output of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – in contrast to most other models which are offered at no cost to the public in their full form by the authorities that created and operate them.

Google is not the only one in starting to use AI to solve difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.