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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 monster hurricane.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am not ready to forecast that strength at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the system moves slowly over very warm ocean waters which is the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model dedicated to tropical cyclones, and currently the initial to beat traditional weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica additional preparation time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model works by spotting patterns that conventional lengthy physics-based weather models may overlook.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and demanding,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are on par with and, in some cases, more accurate than the slower physics-based weather models we’ve relied upon,” he added.

Understanding AI Technology

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

AI training processes mounds of data and extracts trends from them in a manner that its model only takes a few minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to process and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Advances

Nevertheless, the fact that the AI could outperform earlier top-tier 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 sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while the AI is beating all other models on predicting the trajectory of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to talk with the company about how it can make the AI results even more helpful for forecasters by providing additional internal information they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the results of the system is essentially a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all other models which are provided at no cost to the general audience in their entirety by the governments that designed and maintain them.

Google is not alone in adopting AI to solve challenging meteorological problems. The authorities also have their own AI weather models in the works – which have also shown improved skill over earlier traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and flash flooding – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.