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

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the guise of Google’s new DeepMind cyclone prediction system – launched for the initial occasion in June. And, as predicted, Melissa did become a storm of remarkable power that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. Although I am not ready to predict that strength yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

The Way Google’s System Works

The AI system works by spotting patterns that traditional lengthy scientific weather models may overlook.

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

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower physics-based weather models we’ve relied upon,” Lowry added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been used in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning 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 sharp difference to the flagship models that governments have utilized for decades that can require many hours to run and need the largest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could exceed earlier gold-standard legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on extreme strength forecasts wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Wider Industry Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – in contrast to nearly all systems which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The US and European governments also have their own AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as sub-seasonal outlooks and improved early alerts of severe weather and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to fill the gaps in the US weather-observing network.