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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it would soon grow into a major tropical system.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

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: “Approximately 40/50 AI ensemble members indicate Melissa becoming a most intense hurricane. While I am unprepared to forecast that intensity yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over very warm ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, and currently the initial to outperform traditional weather forecasters at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

The Way The Model Functions

Google’s model operates through spotting patterns that conventional lengthy scientific weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower traditional weather models we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, the system is an example of AI training – a technique that has been employed in research fields like meteorology for a long time – and is not generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that the AI could outperform previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

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

Franklin said that while the AI is outperforming all competing systems on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with another storm previously, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can utilize to evaluate the reasons it is producing its conclusions.

“A key concern that troubles me is that although these forecasts seem to be really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which grants experts a peek into its techniques – unlike most systems which are offered free to the public in their full form by the authorities that designed and maintain them.

The company is not alone in adopting AI to address challenging weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the US weather-observing network.