🔗 Share this article

When Tropical Storm 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 a single day the weather system would become a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the form of the tech giant’s new DeepMind hurricane model – released for the initial occasion in June. True to the forecast, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Growing 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: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 storm. While I am unprepared to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and now the initial to outperform traditional meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is top-performing – even beating experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided residents additional preparation time to get ready for the disaster, possibly saving people and assets.

How The System Functions

Google’s model works by spotting patterns that traditional lengthy scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” stated Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in some cases, superior than the slower physics-based weather models we’ve relied upon,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an example of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a manner that its model only requires minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for years that can take hours to run and need the largest supercomputers in the world.

Professional Responses and Upcoming Advances

Still, the fact that Google’s model could outperform previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have dedicated their lives trying to forecast the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The data is now large enough that it’s pretty clear this is not a case of chance.”

He said that while Google DeepMind is beating all other models on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to discuss with the company about how it can enhance the DeepMind output even more helpful for experts by offering additional under-the-hood data they can utilize to assess exactly why it is producing its answers.

“The one thing that troubles me is that although these predictions appear highly accurate, the results of the model is essentially a opaque process,” remarked Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has produced a high-performance weather model which allows researchers a peek into its methods – in contrast to most other models which are offered at no cost to the public in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to solve challenging meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and improved early alerts of tornado outbreaks and sudden deluges – and they are receiving US government funding to do so. A particular firm, WindBorne Systems, is even launching its proprietary atmospheric sensors to address deficiencies in the US weather-observing network.