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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it would soon grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had ever issued this confident forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s recently introduced DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Roughly 40/50 AI simulation runs show Melissa becoming a Category 5 storm. Although I am not ready to predict that strength at this time given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the first AI model dedicated to hurricanes, and now the initial to beat standard weather forecasters at their own game. Through all tropical systems this season, the AI is top-performing – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

How Google’s System Functions

The AI system works by identifying trends that conventional lengthy scientific weather models may overlook.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“This season’s events has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

To be sure, the system is an instance of machine learning – a technique that has been employed in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that authorities have used for decades that can take hours to run and need some of the biggest high-performance systems in the world.

Expert Responses and Upcoming Developments

Still, the fact that the AI could outperform earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a former expert. “The sample is sufficient that it’s pretty clear this is not a case of beginner’s luck.”

He said that while the AI is beating all competing systems on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

During the next break, Franklin stated he intends to talk with Google about how it can enhance the AI results even more helpful for experts by providing additional internal information they can use to assess exactly why it is producing its answers.

“The one thing that troubles me is that while these predictions seem to be really, really good, the output of the model is kind of a black box,” said Franklin.

Broader Sector Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its methods – unlike nearly all other models which are offered at no cost to the general audience in their entirety by the authorities that created and operate them.

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

Future developments in AI weather forecasts appear to involve startup companies tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is even deploying its proprietary weather balloons to fill the gaps in the US weather-observing network.