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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become 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: AI technology in the form of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members show Melissa becoming a most intense hurricane. While I am unprepared to predict that strength at this time due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the system moves slowly over very warm sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

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 13 Atlantic storms this season, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way The System Works

The AI system works by spotting patterns that traditional time-intensive physics-based 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 former 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 less rapid physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an example of AI training – a technique that has been employed in research fields like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its system only takes a few minutes to generate an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for years that can take hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Future Developments

Still, the fact that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he stated he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional internal information they can use to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts seem to be highly accurate, the results of the system is kind of a black box,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a view of its methods – in contrast to nearly all other models which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging weather forecasting problems. The authorities are developing their own AI weather models in the development phase – which have also shown better performance over previous non-AI versions.

The next steps in AI weather forecasts seem to be startup companies taking swings at previously difficult problems such as long-range forecasts and better early alerts of severe weather and flash flooding – and they have secured federal support to do so. One company, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.