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As Tropical Storm Melissa swirled off the coast of Haiti, weather expert Philippe Papin felt certain it would soon escalate to a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that intensity at this time given track uncertainty, that is still plausible.

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

Outperforming Conventional Models

Google DeepMind is the pioneer artificial intelligence system focused on hurricanes, 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 – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the disaster, potentially preserving people and assets.

How The System Functions

Google’s model works by spotting patterns that traditional time-intensive physics-based weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are on par with and, in some cases, more accurate than the slower traditional forecasting tools we’ve relied upon,” Lowry added.

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 weather science for years – and is distinct from generative AI 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 result, and can do so on a standard PC – in sharp difference to the primary systems that governments have used for years that can take hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Still, the fact that the AI could outperform previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The sample is sufficient that it’s evident this is not just chance.”

Franklin noted that while Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It had difficulty with another storm previously, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to discuss with Google about how it can enhance the AI results even more helpful for forecasters by offering additional internal information they can utilize to evaluate exactly why it is producing its answers.

“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.

Wider Industry Trends

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a view of its methods – unlike nearly all other models which are provided free to the general audience in their entirety by the governments that created and operate them.

The company is not the only one in adopting artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their own artificial intelligence systems in the works – which have demonstrated better performance over previous traditional systems.

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 US weather-observing network.