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When Developing Cyclone Melissa swirled off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a category 4 hurricane and begin a turn towards the coast of Jamaica. No forecaster had previously made this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense storm. Although I am unprepared to predict that intensity yet due to path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening is expected as the storm moves slowly over exceptionally hot sea temperatures which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first AI model focused on hurricanes, and now the initial to beat standard weather forecasters at their specialty. Across all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at maximum strength, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica extra time to get ready for the disaster, possibly saving people and assets.

The Way The Model Works

The AI system works by spotting patterns that traditional time-intensive scientific prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, superior than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in research fields like weather science for a long time – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have used for years that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Future Advances

Still, the fact that the AI could outperform earlier gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the world’s strongest storms.

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

Franklin said that while Google DeepMind is beating all other models on predicting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to maximum intensity above the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can utilize to assess exactly why it is producing its conclusions.

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

Broader Industry Developments

Historically, no a commercial entity that has developed a high-performance weather model which grants experts a peek into its methods – in contrast to nearly all systems which are provided at no cost to the public in their entirety by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to solve difficult meteorological problems. The authorities also have their respective artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the US weather-observing network.