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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin had confidence it was about to grow into a major tropical system.

Serving as primary meteorologist on duty, he predicted that in just 24 hours the storm would become a severe hurricane and begin a turn in the direction of the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the guise of the tech giant’s new DeepMind cyclone prediction system – launched for the first time in June. True to the forecast, Melissa evolved into a system of remarkable power that tore through Jamaica.

Growing Reliance on AI Forecasting

Forecasters are heavily relying upon Google DeepMind. During 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 AI simulation runs show Melissa becoming a most intense storm. While I am unprepared 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 drifts over very warm sea temperatures which is the most extreme marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer AI model dedicated to tropical cyclones, and now the initial to beat standard weather forecasters at their own game. Across all tropical systems so far this year, Google’s model is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts recorded in almost 200 years of record-keeping across the region. The confident prediction probably provided residents extra time to get ready for the disaster, possibly saving people and assets.

The Way Google’s System Functions

The AI system operates through identifying trends that traditional time-intensive physics-based weather models may overlook.

“They do it far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve relied upon,” he said.

Understanding AI Technology

It’s important to note, Google DeepMind is an instance of AI training – a technique that has been employed in research fields like weather science for a long time – 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 requires minutes to generate an result, and can operate on a standard PC – in sharp difference to the flagship models that governments have used for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the fact that Google’s model could outperform earlier top-tier traditional systems so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to forecast the most intense weather systems.

“I’m impressed,” said James Franklin, a retired expert. “The sample is sufficient that it’s evident this is not a case of chance.”

Franklin noted that while Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts wrong. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 north of 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 under-the-hood data they can use to assess the reasons it is coming up with its answers.

“The one thing that troubles me is that although these forecasts appear really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a top-level forecasting system which grants experts a peek into its techniques – unlike nearly all other models which are offered free to the general audience in their entirety by the authorities that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The authorities also have their respective AI weather models in the works – which have also shown improved skill over previous non-AI versions.

The next steps in AI weather forecasts seem to be new firms tackling formerly tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and flash flooding – and they have secured US government funding to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.