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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that strength yet given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and now the initial to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving people and assets.

How Google’s Model Works

The AI system operates through identifying trends that traditional time-intensive scientific prediction systems may overlook.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an example 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 large datasets and pulls out patterns from them in a manner that its system only requires minutes to come up with 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 run and require some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

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

“I’m impressed,” commented James Franklin, a former expert. “The data is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He said that although the AI is outperforming all other models on forecasting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering extra under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the output of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its techniques – in contrast to most systems which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address challenging meteorological problems. The authorities also have their own AI weather models in the development phase – which have demonstrated improved skill over previous non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies 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 federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the national monitoring system.