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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and now the initial to beat traditional weather forecasters at their own game. Through all 13 Atlantic storms so far this year, the AI is the best – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the disaster, potentially preserving lives and property.

How Google’s Model Works

Google’s model operates through identifying trends that traditional time-intensive scientific weather models may miss.

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

“What this hurricane season has proven in short order is that the recent AI weather models are competitive with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been employed in research fields like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a such a way that its system only requires minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

He said that while the AI is outperforming all competing systems on predicting the future path of hurricanes worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can make the DeepMind output more useful for experts by providing extra internal information they can utilize to assess 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 model is essentially a black box,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level weather model which allows researchers a view of its techniques – in contrast to most systems which are provided free to the general audience in their full form by the authorities that designed and maintain them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the national monitoring system.