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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 monster hurricane.

As the lead forecaster on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. Although I am unprepared to predict that intensity at this time given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and currently the first to outperform standard weather forecasters at their specialty. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

The Way Google’s System Functions

The AI system works by identifying trends that traditional lengthy scientific weather models may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its system only takes a few minutes to generate an result, and can operate on a desktop computer – in strong contrast to the primary systems that authorities have used for decades that can take hours to process and require the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that the AI could outperform earlier gold-standard legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that while the AI is outperforming all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the AI results even more helpful for experts by providing extra internal information they can utilize to assess the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the output of the system is kind of a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has developed a top-level weather model which grants experts a peek into its methods – unlike nearly all other models which are offered free to the general audience in their entirety by the governments that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and improved advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is even launching its own weather balloons to fill the gaps in the US weather-observing network.