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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a major tropical system.

Serving as primary meteorologist on duty, he predicted that in a single day the weather system would become a severe hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made this confident prediction for quick intensification.

But, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a system of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

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: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa reaching a most intense storm. Although I am not ready to forecast that intensity at this time due to track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Conventional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and currently the initial to beat traditional weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at maximum strength, among the most powerful coastal impacts recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, potentially preserving lives and property.

The Way Google’s System Works

Google’s model works by spotting patterns that traditional lengthy physics-based prediction systems may overlook.

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are on par with and, in some cases, superior than the less rapid traditional weather models we’ve relied upon,” Lowry said.

Understanding AI Technology

To be sure, the system is an example of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from generative AI like ChatGPT.

AI training takes 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 operate on a standard PC – in strong contrast to the primary systems that governments have utilized for decades that can take hours to run and need the largest supercomputers in the world.

Professional Reactions and Upcoming Advances

Nevertheless, the reality that the AI could exceed previous top-tier traditional systems so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

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

Franklin noted that although Google DeepMind is beating all competing systems on forecasting the trajectory of hurricanes globally this year, similar to other systems it occasionally gets high-end intensity forecasts inaccurate. It had difficulty with another storm previously, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can enhance the AI results more useful for experts by offering extra 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 appear really, really good, the output of the system is kind of a black box,” remarked Franklin.

Broader Industry Trends

There has never been a commercial entity that has developed a high-performance weather model which grants experts a peek into its techniques – unlike nearly all other models which are offered free to the public in their entirety by the authorities that created and operate them.

The company is not alone in adopting artificial intelligence to solve challenging weather forecasting problems. The authorities also have their respective artificial intelligence systems in the development phase – which have also shown better performance over earlier non-AI versions.

The next steps in AI weather forecasts seem to be startup companies tackling previously tough-to-solve problems such as long-range forecasts and improved early alerts of severe weather and sudden deluges – and they are receiving US government funding to do so. One company, WindBorne Systems, is even launching its proprietary weather balloons to address deficiencies in the national monitoring system.