🔗 Share this article

When Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

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

However, Papin had an ace up his sleeve: artificial intelligence 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 did become a storm of remarkable power that ravaged Jamaica.

Growing Reliance on AI Predictions

Forecasters are heavily relying upon 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 Google DeepMind simulation runs show Melissa becoming a Category 5 hurricane. While I am unprepared to forecast that intensity yet due to track uncertainty, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system drifts over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the pioneer artificial intelligence system dedicated to hurricanes, and currently the first to outperform traditional weather forecasters at their own game. Across all tropical systems this season, the AI is top-performing – surpassing experts on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, potentially preserving people and assets.

How The System Functions

Google’s model works by spotting patterns that conventional time-intensive scientific weather models may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, the system is an instance of AI training – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its system only requires minutes to come up with an result, and can operate on a desktop computer – in sharp difference to the flagship models that governments 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 Google’s model could outperform previous gold-standard traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“I’m impressed,” commented James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of chance.”

Franklin noted that although Google DeepMind is outperforming all competing systems on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin stated he intends to discuss with Google about how it can make the AI results even more helpful for forecasters by providing extra internal information they can use to evaluate the reasons it is coming up with its conclusions.

“A key concern that troubles me is that while these predictions seem to be really, really good, the results of the model is essentially a opaque process,” remarked Franklin.

Broader Industry Developments

Historically, no a private, for-profit company that has produced a top-level weather model which allows researchers a view of its methods – in contrast to nearly all systems which are provided free to the public in their full form by the authorities that created and operate them.

Google is not alone in adopting artificial intelligence to solve difficult weather forecasting problems. The authorities also have their own AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the national monitoring system.