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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a major tropical system.

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a severe hurricane and start shifting towards the coast of Jamaica. Not a single expert had ever issued such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 AI simulation runs show 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.

“It appears likely that a phase of quick strengthening is expected as the storm drifts over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform traditional weather forecasters at their specialty. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts 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.

The Way The System Works

Google’s model operates through spotting patterns that conventional time-intensive physics-based prediction systems 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 former meteorologist.

“What this hurricane season has demonstrated in short order is that the recent AI weather models are on par with and, in certain instances, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, the system 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 distinct from generative AI like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a such a way that its model only requires 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 decades that can take hours to process and require some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Nevertheless, the reality that Google’s model could outperform earlier top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest weather systems.

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

He said that while Google DeepMind is outperforming all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

In the coming offseason, he stated he plans to discuss with the company about how it can enhance the DeepMind output more useful for forecasters by offering extra under-the-hood data they can use to evaluate exactly why it is producing its answers.

“A key concern that troubles me is that although these forecasts appear highly accurate, the output of the model is kind of a opaque process,” remarked Franklin.

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike most other models which are provided free to the general audience in their full form by the governments that designed and maintain them.

Google is not alone in adopting AI to address challenging meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.