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When Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued this confident prediction for quick intensification.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of astonishing strength that ravaged Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his public discussion that the AI tool was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa reaching a most intense hurricane. Although I am not ready to predict that intensity yet due to track uncertainty, that remains a possibility.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over very warm ocean waters which represent the highest oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Models

The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to beat standard meteorological experts at their own game. Across all tropical systems so far this year, the AI is the best – even beating human forecasters on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the region. Papin’s bold forecast likely gave residents extra time to get ready for the catastrophe, possibly saving people and assets.

The Way Google’s Model Functions

Google’s model operates through spotting patterns that traditional time-intensive physics-based prediction systems may miss.

“They do it much more quickly than their physics-based cousins, and the computing power is less expensive and time consuming,” said Michael Lowry, a former meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower physics-based forecasting tools we’ve relied upon,” he said.

Clarifying Machine Learning

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 distinct from creative artificial intelligence like ChatGPT.

AI training processes mounds of data and pulls out patterns from them in a manner that its system only requires minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have used for years that can require many hours to process and require some of the biggest supercomputers in the world.

Expert Reactions and Upcoming Advances

Nevertheless, the fact that the AI could exceed previous gold-standard legacy models so quickly is truly remarkable to weather scientists who have dedicated their lives trying to predict the most intense storms.

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

He noted that although the AI is beating all other models on predicting the trajectory of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 above the Caribbean.

In the coming offseason, he said he intends to discuss with the company about how it can make the AI results even more helpful for experts by providing additional internal information they can utilize to assess the reasons it is producing its conclusions.

“The one thing that troubles me is that while these forecasts seem to be highly accurate, the results of the system is kind of a black box,” said Franklin.

Wider Sector Developments

There has never been a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its techniques – in contrast to most systems which are offered free to the public in their entirety by the governments that designed and maintain them.

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

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and improved early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.