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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert 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 become a severe hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology 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 evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his confidence: “Approximately 40/50 AI simulation runs show Melissa reaching a Category 5 storm. Although I am unprepared to predict that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, potentially preserving people and assets.

How The Model Works

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of machine learning – a technique that has been used in data-heavy sciences like weather science for years – and is distinct from 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 takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the primary systems that governments have utilized for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that Google’s model could outperform earlier top-tier traditional systems so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the most intense storms.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin said that although the AI is beating all other models on predicting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with Google about how it can make the AI results more useful for experts by offering additional under-the-hood data they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions appear really, really good, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – in contrast to nearly all systems which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities are developing their own artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

The next steps in artificial intelligence predictions appear to involve new firms taking swings at formerly difficult problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.