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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as 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 Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first artificial intelligence system focused on hurricanes, and currently the initial to outperform standard meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – even beating experts on path forecasts.

The hurricane ultimately struck in Jamaica at maximum intensity, one of the strongest landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way The System Works

Google’s model operates through spotting patterns that traditional time-intensive scientific prediction systems may overlook.

“They do it much more quickly than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

It’s important to note, the system is an example of AI training – a method that has been employed in data-heavy sciences like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can operate on a desktop computer – in sharp difference to the primary systems that authorities have used for years that can take hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the world’s strongest storms.

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

He noted that while Google DeepMind is beating all competing systems on predicting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can enhance the AI results more useful for experts by offering extra under-the-hood data they can use to assess the reasons it is producing its answers.

“The one thing that nags at me is that although these forecasts seem to be highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its methods – in contrast to most systems which are provided at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in starting to use AI to solve challenging meteorological problems. The authorities are developing their own AI weather models in the works – which have demonstrated improved skill over earlier 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 improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to address deficiencies in the national monitoring system.