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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold prediction for rapid strengthening.

However, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa reaching a Category 5 storm. Although I am not ready to forecast that intensity yet due to path variability, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer artificial intelligence system focused on hurricanes, and currently the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms this season, Google’s model is the best – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the region. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How The System Functions

The AI system operates through spotting patterns that conventional time-intensive physics-based weather models may overlook.

“They do it far faster than their traditional counterparts, and the processing requirements is less expensive and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower traditional weather models we’ve relied upon,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an instance of machine learning – a technique that has been employed in research fields like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes large datasets and extracts trends from them in a manner that its system only takes a few minutes to come up with an result, and can do so on a standard PC – in strong contrast to the flagship models that authorities have utilized for years that can take hours to run and need some of the biggest high-performance systems in the world.

Professional Reactions and Upcoming Developments

Nevertheless, the fact that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is sufficient that it’s pretty clear this is not just chance.”

Franklin said that although Google DeepMind is beating all other models on predicting the future path of storms globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of 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 experts by offering extra under-the-hood data they can use to evaluate the reasons it is producing its conclusions.

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

Wider Sector Developments

There has never been a commercial entity that has produced a high-performance forecasting system which allows researchers a view of its techniques – in contrast to nearly all systems which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting AI to address difficult weather forecasting problems. The authorities also have their respective AI weather models in the works – which have demonstrated improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is even deploying its own atmospheric sensors to address deficiencies in the national monitoring system.