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As Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as 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. Not a single expert had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a system of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 AI ensemble members indicate Melissa reaching a Category 5 storm. While I am unprepared to forecast that strength yet due to path variability, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system moves slowly over very warm ocean waters which represent the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

Google DeepMind is the pioneer AI model focused on hurricanes, and now the first to outperform traditional meteorological experts at their specialty. Through all tropical systems so far this year, the AI is top-performing – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful landfalls recorded in almost 200 years of data collection across the region. Papin’s bold forecast probably provided people in Jamaica extra time to prepare for the disaster, possibly saving people and assets.

The Way Google’s System Functions

Google’s model operates through spotting patterns that traditional lengthy scientific prediction systems may miss.

“They do it far faster than their physics-based cousins, and the computing power is less expensive and demanding,” stated Michael Lowry, a former meteorologist.

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

Clarifying Machine Learning

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 not generative AI like ChatGPT.

Machine learning takes large datasets and pulls out patterns from them in a such a way that its model only requires minutes to come up with an result, and can do so on a desktop computer – in sharp difference to the flagship models that governments have utilized for decades that can require many hours to process and need the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Still, the reality that the AI could exceed earlier gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to predict the most intense weather systems.

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

He noted that while Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on high-end intensity predictions inaccurate. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can make the AI results more useful for experts by offering extra under-the-hood data they can utilize to evaluate the reasons it is coming up with its answers.

“The one thing that nags at me is that while these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which allows researchers a peek into its methods – in contrast to nearly all systems which are offered free to the general audience in their full form by the authorities that designed and maintain them.

The company is not the only one in adopting artificial intelligence to address difficult meteorological problems. The US and European governments also have their respective artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they are receiving US government funding to pursue this. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.