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When Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the weather system would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for rapid strengthening.

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

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am not ready to forecast that strength at this time due to track uncertainty, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and now the first to beat standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is top-performing – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, 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 catastrophe, possibly saving lives and property.

How The System Works

The AI system operates through spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has proven in short order is that the recent AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a method that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the primary systems that authorities have utilized for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Future Developments

Still, the fact that the AI could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense storms.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He said that while Google DeepMind is outperforming all competing systems on forecasting the trajectory of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with another storm earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to assess the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which allows researchers a peek into its techniques – in contrast to nearly all systems which are provided free to the public in their entirety by the authorities that created and operate them.

Google is not alone in starting to use AI to solve difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over previous traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.