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When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the first to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is the best – surpassing human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, 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, potentially preserving people and assets.

The Way Google’s System Functions

Google’s model works by spotting patterns that conventional lengthy physics-based weather models may miss.

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

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been used in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can require many hours to run and need the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the fact that the AI could exceed earlier gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

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

He said that while the AI is outperforming all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can enhance the DeepMind output more useful for forecasters by providing extra internal information they can use to evaluate the reasons it is producing its answers.

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

Wider Sector Developments

There has never been a commercial entity that has developed a top-level weather model which grants experts a view of its techniques – unlike nearly all systems which are provided free to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the US weather-observing network.