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

Serving as lead forecaster on duty, he predicted that in a single day the storm would become a category 4 hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 storm. Although I am unprepared to forecast that intensity yet given path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system moves slowly over exceptionally hot sea temperatures which represent the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and now the initial to beat traditional weather forecasters at their own game. Through all tropical systems this season, Google’s model is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided residents extra time to get ready for the disaster, potentially preserving lives and property.

How The System Functions

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

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

“What this hurricane season has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes 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 desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Future Developments

Still, the fact that the AI could exceed previous gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is now large enough that it’s evident this is not just chance.”

He said that while Google DeepMind is beating all other models on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, Franklin stated he plans to discuss with Google about how it can enhance the DeepMind output more useful for experts by offering extra under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that nags at me is that although these predictions seem to be highly accurate, the output of the system is kind of a black box,” said Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all systems which are offered at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address challenging meteorological problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even launching its own weather balloons to address deficiencies in the national monitoring system.