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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

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

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Predictions

Forecasters are increasingly leaning hard on the AI system. 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 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to forecast that strength at this time given track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents additional preparation time to get ready for the disaster, potentially preserving lives and property.

How Google’s System Works

Google’s model works by spotting patterns that conventional time-intensive scientific prediction systems may overlook.

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

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for years that can require many hours to process and require the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Nevertheless, the reality that the AI could outperform previous gold-standard traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that while the AI is outperforming all other models on forecasting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can enhance the DeepMind output more useful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the results of the model is essentially a black box,” said Franklin.

Broader Industry Trends

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its techniques – unlike nearly all systems which are offered free to the general audience in their entirety by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.