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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he predicted that in a single day the storm would intensify into a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made this confident prediction for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of Google’s recently introduced DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa evolved into a system of astonishing strength that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Meteorologists are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. Although I am not ready to predict that intensity at this time due to track uncertainty, that remains a possibility.

“There is a high probability that a period of quick strengthening is expected as the storm moves slowly over very warm sea temperatures which represent the most extreme marine thermal energy in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and now the first to beat traditional weather forecasters at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving lives and property.

How Google’s System Functions

The AI system works by spotting patterns that traditional lengthy physics-based weather models may miss.

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

“What this hurricane season has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based forecasting tools we’ve traditionally leaned on,” he said.

Clarifying AI Technology

It’s important to note, the system is an instance of machine learning – a method that has been used in research fields like weather science for years – and is not generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the flagship models that governments have used for decades that can require many hours to process and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Still, the reality that the AI could exceed previous gold-standard traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense storms.

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

He said that while Google DeepMind is outperforming all other models on predicting the future path of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he said he plans to talk with the company about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to evaluate exactly why it is producing its conclusions.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the output of the system is essentially a opaque process,” said Franklin.

Broader Sector Trends

Historically, no a commercial entity that has produced a high-performance weather model which allows researchers a peek into its methods – unlike nearly all systems which are offered free to the general audience in their full form by the authorities that designed and maintain them.

Google is not the only one in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve 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 deploying its own weather balloons to address deficiencies in the US weather-observing network.