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

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that strength yet given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the first AI model focused on tropical cyclones, and now the first to outperform traditional weather forecasters at their specialty. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The System Works

The AI system operates through identifying trends that traditional lengthy physics-based prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“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 physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been employed in research fields like meteorology for a long time – 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 result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that Google’s model could exceed previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of chance.”

He said that while Google DeepMind is beating all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he stated he plans to discuss with Google about how it can make the AI results even more helpful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions appear really, really good, the results of the model is kind of a black box,” remarked Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has developed a top-level weather model which grants experts a view of its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the US weather-observing network.