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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin had confidence it would soon grow into a major tropical system.

Serving as primary meteorologist on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had previously made such a bold forecast for rapid strengthening.

But, Papin possessed a secret advantage: AI technology in the form of Google’s recently introduced DeepMind hurricane model – launched 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 Predictions

Forecasters are heavily relying upon the AI system. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a most intense hurricane. While I am unprepared to forecast that strength yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of rapid intensification will occur as the storm moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional Models

The AI model is the first AI model focused on tropical cyclones, and currently the first to outperform standard meteorological experts at their own game. Through all 13 Atlantic storms this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in almost 200 years of record-keeping across the region. Papin’s bold forecast likely gave people in Jamaica extra time to prepare for the catastrophe, possibly saving people and assets.

The Way The Model Functions

The AI system works by identifying trends that traditional lengthy scientific prediction systems may miss.

“They do it far faster than their traditional counterparts, and the processing requirements is more affordable and time consuming,” stated Michael Lowry, a ex meteorologist.

“This season’s events has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, superior than the slower traditional weather models we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been used in data-heavy sciences like meteorology for years – and is not generative AI like ChatGPT.

Machine learning processes mounds of data and pulls out patterns from them in a such a way 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 utilized for years that can take hours to process and require some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Still, the fact that Google’s model could exceed previous top-tier legacy models so quickly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former expert. “The data is sufficient that it’s evident this is not just beginner’s luck.”

He said that while the AI is outperforming all other models on forecasting the future path of hurricanes globally this year, similar to other systems it sometimes errs on extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to category 5 above the Caribbean.

During the next break, he said he plans to discuss with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional under-the-hood data they can use to assess exactly why it is coming up with its answers.

“A key concern that nags at me is that while these predictions appear highly accurate, the results of the system is essentially a black box,” remarked Franklin.

Broader Sector Trends

Historically, no a private, for-profit company that has produced a high-performance weather model which grants experts a view of its methods – unlike nearly all other models which are offered at no cost to the public in their entirety by the governments that designed and maintain them.

Google is not the only one in starting to use AI to address challenging weather forecasting problems. The US and European governments are developing their respective AI weather models in the works – which have also shown improved skill over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms taking swings at previously tough-to-solve problems such as long-range forecasts and better 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 own weather balloons to fill the gaps in the US weather-observing network.