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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting in the direction of the Jamaican shoreline. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. 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 AI ensemble members show Melissa reaching a Category 5 hurricane. While I am unprepared to forecast that intensity yet given path variability, that is still plausible.

“It appears likely that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which is the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the initial to beat traditional weather forecasters at their own game. Across all tropical systems so far this year, Google’s model is the best – even beating experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the disaster, potentially preserving lives and property.

How The System Functions

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the less rapid physics-based weather models we’ve relied upon,” Lowry said.

Understanding Machine Learning

To be sure, the system is an instance of AI training – a method that has been employed in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can operate on a desktop computer – in strong contrast to the flagship models that governments have utilized for years that can take hours to process and need some of the biggest supercomputers in the world.

Professional Responses and Upcoming Developments

Still, the fact that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest storms.

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

He noted that while the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, Franklin said he intends to talk with the company about how it can make the AI results even more helpful for forecasters by providing additional internal information they can use to assess exactly why it is coming up with its answers.

“A key concern that troubles me is that while these predictions seem to be really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Sector Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – in contrast to nearly all systems which are offered free to the public in their full form by the authorities that designed and maintain them.

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

Future developments in artificial intelligence predictions appear to involve new firms tackling formerly difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.