🔗 Share this article

When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

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

Increasing Reliance on AI Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over exceptionally hot ocean waters which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, among the most powerful landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

How Google’s System Functions

Google’s model works by spotting patterns that conventional lengthy scientific weather models may overlook.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data 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 sharp difference to the flagship models that governments have used for years that can require many hours to run and require some of the biggest high-performance systems in the world.

Expert Responses and Future Developments

Nevertheless, the reality that the AI could exceed earlier gold-standard traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“I’m impressed,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not a case of beginner’s luck.”

He said that although Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the AI results even more helpful for experts by offering extra internal information they can use to assess the reasons it is producing its answers.

“The one thing that troubles me is that while these forecasts seem to be really, really good, the results of the system is kind of a opaque process,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its methods – in contrast to nearly all other models which are offered free to the public in their full form by the governments that created and operate them.

Google is not alone in adopting AI to solve difficult weather forecasting problems. The US and European governments also have 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 taking swings at previously difficult problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.