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As Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it would soon escalate to a major tropical system.

Serving as lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 hurricane. Although I am unprepared to predict that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over very warm sea temperatures which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first artificial intelligence system focused on hurricanes, and now the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

Melissa eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving lives and property.

How The System Functions

Google’s model operates through spotting patterns that conventional time-intensive physics-based weather models may miss.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the slower traditional weather models we’ve relied upon,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been used in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

Machine learning processes mounds of data and extracts trends from them in a such a way that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Future Developments

Still, the fact that Google’s model could outperform earlier top-tier traditional systems so quickly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not a case of chance.”

Franklin noted that while the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he said he plans to talk with the company about how it can make the AI results more useful for experts by providing additional internal information they can use to assess exactly why it is coming up with its conclusions.

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

Wider Sector Trends

There has never been a commercial entity that has produced a top-level forecasting system which allows researchers a view of its techniques – in contrast to most other models which are offered free to the public in their full form by the governments that designed and maintain them.

Google is not the only one in starting to use artificial intelligence to address challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have demonstrated better performance over previous non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.