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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would intensify into a severe hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a key factor for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a most intense hurricane. Although I am unprepared to predict that strength at this time due to track uncertainty, that is still plausible.

“There is a high probability that a phase of quick strengthening is expected as the system moves slowly over exceptionally hot sea temperatures which is the most extreme marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

Google DeepMind is the first artificial intelligence system focused on hurricanes, and currently the initial to beat traditional weather forecasters at their specialty. Through all tropical systems this season, Google’s model is top-performing – even beating experts on track predictions.

Melissa eventually made landfall in Jamaica at category 5 strength, among the most powerful landfalls recorded in nearly two centuries of data collection across the region. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving people and assets.

How The System Works

Google’s model works by spotting patterns that traditional lengthy scientific prediction systems may miss.

“The AI performs far faster than their physics-based cousins, and the processing requirements is less expensive and demanding,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has proven in short order is that the newcomer AI weather models are on par with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry said.

Clarifying AI Technology

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been used in data-heavy sciences like weather science for years – and is not creative artificial intelligence like ChatGPT.

AI training takes mounds of data and pulls out patterns from them in a manner that its model only requires minutes to come up with an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for years that can require many hours to run and need the largest high-performance systems in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed earlier top-tier traditional systems so rapidly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense storms.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is now large enough that it’s evident this is not a case of chance.”

He noted that while Google DeepMind is outperforming all other models on forecasting the trajectory of storms globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing quick strengthening to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by offering additional under-the-hood data they can utilize to evaluate exactly why it is producing its conclusions.

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

Broader Industry Developments

There has never been a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its methods – unlike most systems which are provided free to the public in their full form by the governments that designed and maintain them.

The company is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the works – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.