🔗 Share this article

When Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on AI Forecasting

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 hurricane. While I am unprepared to predict that intensity at this time given track uncertainty, that remains a possibility.

“It appears likely that a period 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.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to outperform standard weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents extra time to prepare for the disaster, possibly saving lives and property.

How The Model Works

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

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Developments

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

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

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm previously, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to talk with the company about how it can make the DeepMind output even more helpful for experts by offering additional internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that while these predictions appear highly accurate, the results of the system is kind of a opaque process,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a view of its methods – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve challenging weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.