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

As the primary meteorologist on duty, he predicted that in a single day the weather system would become a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of Google’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs indicate Melissa becoming a Category 5 hurricane. While I am not ready to forecast that intensity at this time given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the storm moves slowly over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system focused on tropical cyclones, and currently the initial to outperform standard meteorological experts at their specialty. Through all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts ever documented in nearly two centuries of record-keeping across the region. The confident prediction likely gave residents extra time to get ready for the disaster, possibly saving lives and property.

How Google’s System Works

Google’s model operates through identifying trends that conventional time-intensive physics-based weather models may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve relied upon,” he added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is not generative AI like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a such a way 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 primary systems that governments have used for years that can take hours to run and require some of the biggest supercomputers in the world.

Expert Responses and Future Advances

Nevertheless, the reality that the AI could exceed earlier top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not just chance.”

He said that although Google DeepMind is outperforming all competing systems on predicting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the AI results even more helpful for experts by providing additional 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 results of the system is essentially a black box,” remarked Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a top-level weather model which allows researchers a view of its methods – unlike most other models which are offered free to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in adopting AI to solve difficult meteorological problems. The authorities also have 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 taking swings at formerly difficult problems such as long-range forecasts and better early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.