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When Tropical Storm Melissa was churning south of Haiti, weather expert Philippe Papin had confidence it was about to 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 start shifting in the direction of the Jamaican shoreline. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’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 tore through Jamaica.

Increasing Reliance on AI Forecasting

Forecasters are heavily relying upon 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: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. Although I am not ready to forecast that strength at this time given path variability, that remains a possibility.

“There is a high probability that a phase of quick strengthening will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model focused on hurricanes, and now the initial to outperform traditional weather forecasters at their own game. Across all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, among the most powerful landfalls recorded in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the disaster, possibly saving lives and property.

The Way Google’s Model Functions

Google’s model works by spotting patterns that conventional time-intensive scientific weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” stated Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent AI weather models are on par with and, in some cases, more accurate than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he said.

Understanding AI Technology

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in research fields like meteorology for a long time – and is not creative artificial intelligence like ChatGPT.

AI training processes large datasets and extracts trends from them in a such a way that its system only takes a few minutes to come up with an result, and can do so on a desktop computer – in strong contrast to the flagship models that governments have utilized for decades that can take hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Future Developments

Nevertheless, the fact that the AI could outperform earlier gold-standard legacy models 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 expert. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

He noted that while Google DeepMind is beating all competing systems on predicting the trajectory of storms globally this year, like many AI models it occasionally gets high-end intensity forecasts wrong. It had difficulty with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, Franklin said he plans to discuss with the company about how it can make the AI results more useful for experts by offering additional internal information they can utilize to assess exactly why it is producing its answers.

“A key concern that nags at me is that while these forecasts appear really, really good, the output of the system is essentially a black box,” said Franklin.

Broader Industry Developments

Historically, no a commercial entity that has produced a high-performance forecasting system which grants experts a peek into its methods – unlike nearly all other models which are provided free to the general audience in their full form by the governments that designed and maintain them.

The company is not alone in adopting artificial intelligence to address difficult weather forecasting problems. The authorities are developing their respective artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

The next steps in AI weather forecasts seem to be new firms taking swings at formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its own weather balloons to address deficiencies in the national monitoring system.