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When Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted 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 this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a key factor for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a Category 5 hurricane. While I am not ready to forecast that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification will occur as the system drifts over very warm ocean waters which is the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and now the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms this season, Google’s model is the best – even beating experts on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave residents additional preparation time to prepare for the catastrophe, potentially preserving people and assets.

The Way The System Works

The AI system works by spotting patterns that traditional time-intensive scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve relied upon,” he added.

Understanding Machine Learning

To be sure, the system is an instance of AI training – a technique that has been used in research fields like weather science for a long time – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a manner that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that authorities have utilized for years that can take hours to process and require the largest supercomputers in the world.

Professional Reactions and Future Advances

Still, the fact that Google’s model could exceed previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest storms.

“I’m impressed,” commented James Franklin, a former expert. “The data is sufficient that it’s evident this is not just chance.”

He noted that while the AI is beating all competing systems on predicting the future path of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he intends to discuss with Google about how it can enhance the AI results even more helpful for experts by offering extra internal information they can use to evaluate the reasons it is producing its answers.

“A key concern that nags at me is that although these forecasts seem to be highly accurate, the output of the model is kind of a black box,” said Franklin.

Broader Industry Developments

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers a peek into its methods – unlike nearly all other models which are provided at no cost to the general audience in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve challenging meteorological problems. The US and European governments are developing their own artificial intelligence systems in the development phase – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve new firms tackling formerly difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to address deficiencies in the US weather-observing network.