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When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in just 24 hours the storm would intensify into a severe hurricane and start shifting in the direction of the Jamaican shoreline. Not a single expert had previously made such a bold forecast for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters 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 certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. While I am unprepared to forecast that strength yet given path variability, that is still plausible.

“It appears likely that a phase of rapid intensification is expected as the storm drifts over very warm sea temperatures which represent the highest oceanic heat content in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the pioneer AI model focused on tropical cyclones, and currently the first to outperform traditional meteorological experts at their specialty. Across all tropical systems so far this year, the AI is top-performing – even beating experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. Papin’s bold forecast probably provided people in Jamaica additional preparation time to prepare for the disaster, possibly saving people and assets.

How The Model Works

The AI system works by spotting patterns that traditional time-intensive scientific weather models may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is more affordable and time consuming,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are on par with and, in certain instances, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, the system is an instance of machine learning – a technique that has been used in research fields like meteorology for years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning processes large datasets and extracts trends from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in strong contrast to the flagship models that authorities have used for decades that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Responses and Future Advances

Still, the fact that the AI could outperform earlier top-tier legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to forecast the most intense storms.

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

He said that although the AI is beating all competing systems on predicting the trajectory of storms worldwide this year, like many AI models it sometimes errs on high-end intensity forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, he stated he plans to talk with the company about how it can make the DeepMind output more useful for forecasters by providing additional internal information they can utilize to assess the reasons it is coming up with its answers.

“A key concern that troubles me is that although these predictions appear really, really good, the results of the model is essentially a black box,” said Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a peek into its techniques – unlike nearly all other models which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to address challenging weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the development phase – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms taking swings at previously difficult problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is also launching its proprietary atmospheric sensors to fill the gaps in the US weather-observing network.