🔗 Share this article

As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as lead forecaster on duty, he predicted that in a single day the weather system would become a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had ever issued 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. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI simulation runs indicate Melissa reaching a Category 5 storm. While I am not ready to predict that intensity at this time due to path variability, that is still plausible.

“It appears likely that a period of rapid intensification is expected as the system drifts over very warm sea temperatures which represent the most extreme oceanic heat content in the entire Atlantic basin.”

Outperforming Traditional Systems

The AI model is the pioneer artificial intelligence system dedicated to tropical cyclones, and currently the initial to beat standard meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – surpassing human forecasters on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving people and assets.

The Way The Model Functions

The AI system works by identifying trends that traditional lengthy physics-based prediction systems may miss.

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

“This season’s events has proven in quick time is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been employed in research fields like weather science for years – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and require the largest high-performance systems in the world.

Professional Responses and Upcoming Advances

Nevertheless, the fact that Google’s model could outperform earlier top-tier legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” commented James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not just beginner’s luck.”

He said that although Google DeepMind is beating all other models on predicting the future path of storms worldwide this year, like many AI models it occasionally gets extreme strength predictions inaccurate. It had difficulty 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 stated he intends to discuss with the company about how it can enhance the AI results more useful for experts by offering additional under-the-hood data they can use to evaluate the reasons it is producing its answers.

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

Wider Sector Trends

Historically, no a commercial entity that has produced a top-level forecasting system which allows researchers a view of its methods – in contrast to most systems which are provided at no cost to the public in their entirety by the authorities that created and operate them.

Google is not the only one in starting to use artificial intelligence to address challenging weather forecasting problems. The authorities also have their respective AI weather models in the development phase – which have demonstrated improved skill over previous traditional systems.

The next steps in AI weather forecasts appear to involve new firms taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. A particular firm, WindBorne Systems, is also deploying its own atmospheric sensors to fill the gaps in the US weather-observing network.