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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as primary meteorologist 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. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’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 ravaged Jamaica.

Growing 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 Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 storm. Although I am unprepared to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now 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 – surpassing experts on path forecasts.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

How The Model Works

Google’s model operates through spotting patterns that traditional lengthy physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

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

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the primary systems that authorities have utilized for years that can take hours to process and need the largest high-performance systems in the world.

Professional Reactions and Future Advances

Nevertheless, the fact that the AI could exceed previous gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” said 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 predicting the future path of storms worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, he stated he intends to talk with Google about how it can make the AI results more useful for forecasters by providing additional under-the-hood data they can utilize to assess the reasons it is producing its conclusions.

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

Broader Sector Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all other models which are provided free to the general audience in their full form by the authorities that created and operate them.

The company is not alone in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.