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As Tropical Storm Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the coast of Jamaica. No forecaster had previously made such a bold prediction for quick intensification.

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

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa becoming a Category 5 storm. While I am unprepared to forecast that strength at this time given track uncertainty, that is still plausible.

“It appears likely that a period of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the first AI model focused on hurricanes, and now the first to beat traditional meteorological experts at their own game. Through all tropical systems so far this year, the AI is top-performing – surpassing human forecasters on path forecasts.

The hurricane eventually made landfall 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. The confident prediction likely gave people in Jamaica extra time to get ready for the disaster, potentially preserving people and assets.

The Way The Model Functions

Google’s model works by spotting patterns that conventional lengthy scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in quick time is that the newcomer artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

It’s important to note, the system is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its model only takes a few minutes to come up with an answer, and can operate on a standard PC – in sharp difference to the flagship models that authorities have used for years that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Reactions and Future Developments

Nevertheless, the fact that the AI could outperform previous top-tier legacy models so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a retired expert. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin said that while Google DeepMind is beating all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it sometimes errs on high-end intensity forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was similarly experiencing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can make the DeepMind output even more helpful for experts by providing extra under-the-hood data they can utilize to evaluate exactly why it is coming up with its conclusions.

“A key concern that troubles me is that while these forecasts appear really, really good, the results of the system is essentially a black box,” said Franklin.

Broader Sector Developments

Historically, no a commercial entity that has developed a top-level weather model which grants experts a peek into its techniques – unlike most systems which are provided at no cost to the public in their entirety by the governments that designed and maintain them.

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

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and improved 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 proprietary weather balloons to address deficiencies in the national monitoring system.