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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“There is a high probability that a period of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms this season, the AI is the best – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to get ready for the disaster, possibly saving lives and property.

The Way Google’s System Works

Google’s model operates through identifying trends that traditional lengthy physics-based prediction systems may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way that its system 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 utilized for years that can take hours to run and need some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that the AI could exceed previous top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

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

He noted that although the AI is outperforming all competing systems on predicting the future path of storms globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

During the next break, he said he intends to talk 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 evaluate the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Industry Developments

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a view of its techniques – unlike nearly all other models which are provided at no cost to the public in their full form by the governments that created and operate them.

Google is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over previous non-AI versions.

The next steps in artificial intelligence predictions seem to be startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.