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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am not ready to forecast that strength yet given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and now the first to outperform standard weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Works

The AI system works by spotting patterns that conventional time-intensive 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,” stated Michael Lowry, a former forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences 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 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 utilized for decades that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on forecasting the trajectory of storms worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin said he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that while these forecasts appear highly accurate, the results of the model is kind of a black box,” said Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a high-performance weather model which allows researchers a view of its methods – in contrast to most systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting AI to address challenging meteorological problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.