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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted 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 this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind hurricane model – launched for the initial occasion in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 AI ensemble members show Melissa reaching a most intense hurricane. Although I am not ready to forecast that intensity yet given track uncertainty, that is still plausible.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over very warm sea temperatures which is the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

The AI model is the first AI model focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their own game. Through all tropical systems this season, Google’s model is the best – surpassing human forecasters on path forecasts.

The hurricane ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls recorded in nearly two centuries of record-keeping across the Atlantic basin. Papin’s bold forecast probably provided residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The Model Works

Google’s model works by identifying trends that conventional lengthy physics-based prediction systems may miss.

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

“What this hurricane season has demonstrated in short order is that the newcomer artificial intelligence systems are on par with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a method that has been employed in research fields like meteorology for a long time – and is distinct from generative AI like ChatGPT.

Machine learning processes large datasets and extracts trends 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 standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to run and require some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Nevertheless, the fact that the AI could outperform previous gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the most intense weather systems.

“It’s astonishing,” commented James Franklin, a retired expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin noted that although the AI is beating all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts 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 stated he plans 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 utilize to evaluate the reasons it is producing its answers.

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

Wider Industry Trends

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a view of its techniques – unlike most other models which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

The company is not alone in starting to use artificial intelligence to address difficult weather forecasting problems. The US and European governments are developing their respective AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in artificial intelligence predictions seem to be startup companies tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.