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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. True to the forecast, Melissa did become a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a most intense hurricane. Although I am not ready to predict that strength yet due to path variability, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the system moves slowly over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the pioneer AI model dedicated to tropical cyclones, and currently the initial to outperform standard weather forecasters at their own game. Across all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica additional preparation time to prepare for the catastrophe, possibly saving lives and property.

How The Model Works

Google’s model operates through 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 less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated 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 forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in data-heavy sciences like meteorology for a long time – and is distinct from generative AI like ChatGPT.

AI training processes mounds of data and extracts trends from them in a such a way that its system only takes a few minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can require many hours to process and need some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the reality that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the world’s strongest weather systems.

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

He noted that while the AI is beating all other models on forecasting the future path of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to maximum intensity above the Caribbean.

During the next break, he stated he intends to discuss with Google about how it can make the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is producing its conclusions.

“The one thing that nags at me is that although these forecasts appear really, really good, the output of the model is kind of a black box,” said Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has developed a high-performance weather model which grants experts a view of its methods – in contrast to most other models which are provided at no cost to the general audience in their full form by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective artificial intelligence systems in the works – which have demonstrated improved skill over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be new firms taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.