🔗 Share this article

When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: 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 did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence 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 most intense storm. Although I am unprepared to predict that intensity yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on hurricanes, and currently the first to beat standard meteorological experts at their own game. Across all tropical systems this season, the AI is the best – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Functions

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

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a technique that has been employed in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a manner that its system only requires minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for years that can take hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier legacy models so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

Franklin noted that although the AI is beating all competing systems on predicting the future path of storms globally this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with Google about how it can make the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that while these forecasts appear really, really good, the output of the system is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a view of its techniques – unlike most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

The company is not alone in starting to use AI to address difficult weather forecasting problems. The US and European governments also have their own AI weather models in the development phase – which have also shown improved skill over previous traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.