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As Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

Serving as lead forecaster on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a system of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a most intense storm. While I am not ready to predict that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of rapid intensification will occur as the system drifts over very warm ocean waters which is the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the first to outperform traditional weather forecasters at their specialty. Through all 13 Atlantic storms this season, the AI is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

How The System Functions

Google’s model operates through spotting patterns that conventional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex meteorologist.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in some cases, superior than the slower physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

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

AI training takes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in sharp difference to the primary systems that governments have used for decades that can require many hours to run and require some of the biggest high-performance systems in the world.

Professional Responses and Upcoming Developments

Nevertheless, the reality that the AI could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“I’m impressed,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not just beginner’s luck.”

Franklin said that while Google DeepMind is beating all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin stated he intends to talk with the company about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can utilize to assess the reasons it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be really, really good, the results of the model is kind of a opaque process,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its techniques – unlike most other models which are offered at no cost to the general audience in their full form by the governments that designed and maintain them.

The company is not the only one in adopting AI to address difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to fill the gaps in the US weather-observing network.