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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon escalate to a monster hurricane.

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 ever issued such a bold forecast for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am unprepared to predict that strength at this time given path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Systems

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to beat traditional meteorological experts at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving lives and property.

How The Model Works

The AI system works by identifying trends that traditional lengthy physics-based weather models may overlook.

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

“This season’s events has proven in quick time is that the newcomer artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Understanding Machine Learning

To be sure, Google DeepMind is an example of AI training – a method that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have used for years that can take hours to run and require the largest high-performance systems in the world.

Expert Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired expert. “The data is sufficient that it’s evident this is not a case of beginner’s luck.”

Franklin noted that although Google DeepMind is beating all other models on forecasting the future path of storms globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he plans to talk with the company about how it can enhance the AI results more useful for experts by providing additional internal information they can use to assess the reasons it is producing its conclusions.

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

Wider Industry Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – unlike most systems which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown better performance over previous traditional systems.

The next steps in AI weather forecasts seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.