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As Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. While I am unprepared to forecast that intensity at this time given path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the system drifts over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Across all tropical systems this season, the AI is top-performing – even beating human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum intensity, one of the strongest landfalls recorded 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 catastrophe, potentially preserving lives and property.

How Google’s System Functions

Google’s model works by spotting patterns that traditional time-intensive physics-based prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and demanding,” stated Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based weather models we’ve relied upon,” he added.

Clarifying Machine Learning

It’s important to note, the system is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can operate on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can require many hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

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

Franklin said that although Google DeepMind is outperforming all competing systems on predicting the future path of storms worldwide this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can enhance the AI results even more helpful for experts by providing additional internal information they can utilize to assess the reasons it is producing its answers.

“A key concern that troubles me is that although these predictions seem to be really, really good, the results of the system is kind of a black box,” said Franklin.

Broader Sector Trends

There has never been a private, for-profit company that has produced a high-performance forecasting system which grants experts a view of its methods – in contrast to most other models which are offered free to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to solve difficult meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.