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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

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

Increasing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa reaching a most intense hurricane. While I am unprepared to predict that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first AI model dedicated to hurricanes, and currently the initial to beat traditional weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – even beating human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving lives and property.

The Way The System Functions

The AI system operates through identifying trends that conventional time-intensive physics-based prediction systems may miss.

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

“This season’s events has proven in short order is that the recent AI weather models are competitive with and, in some cases, more accurate than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

To be sure, the system is an instance of AI training – a method that has been employed in research fields like meteorology for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require some of the biggest high-performance systems in the world.

Professional Reactions and Future Developments

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not just chance.”

Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the trajectory of hurricanes 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 rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can make the DeepMind output more useful for experts by providing extra internal information they can utilize to assess exactly why it is coming up with its answers.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a top-level forecasting system which grants experts a peek into its methods – in contrast to most 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 AI to address challenging weather forecasting problems. The authorities also have their respective AI weather models in the works – which have demonstrated better performance over previous non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the national monitoring system.