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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over very warm ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and currently the initial to outperform standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, potentially preserving lives and property.

How The System Functions

Google’s model works by identifying trends that traditional time-intensive scientific prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” Lowry added.

Understanding Machine Learning

To be sure, the system is an example of machine learning – a method that has been used in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only requires minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have utilized for decades that can require many hours to process and need the largest supercomputers in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that the AI could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not a case of chance.”

He noted that although the AI is beating all other models on forecasting the trajectory of storms globally this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

During the next break, he stated he intends to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering additional under-the-hood data they can utilize to assess exactly why it is producing its answers.

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

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its methods – unlike most systems which are offered at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not alone in adopting artificial intelligence to address difficult meteorological problems. The US and European governments also have their respective AI weather models in the works – which have also shown improved skill over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling formerly difficult problems such as long-range forecasts and improved early alerts of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its proprietary weather balloons to fill the gaps in the US weather-observing network.