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As Developing Cyclone Melissa was churning south of Haiti, meteorologist Philippe Papin felt certain it would soon grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn in the direction of the Jamaican shoreline. No forecaster had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: AI technology in the form of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa did become a storm of astonishing strength that ravaged Jamaica.

Growing Dependence on Artificial Intelligence Predictions

Meteorologists are increasingly leaning hard on the AI system. During 25 October, Papin explained in his official briefing that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs indicate Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength yet due to path variability, that remains a possibility.

“It appears likely that a period of rapid intensification is expected as the system moves slowly over exceptionally hot ocean waters which represent the highest marine thermal energy in the whole Atlantic basin.”

Outperforming Traditional Models

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard meteorological experts at their own game. Through all 13 Atlantic storms this season, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane ultimately struck in Jamaica at category 5 strength, one of the strongest coastal impacts ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the disaster, possibly saving people and assets.

How The System Functions

Google’s model works by identifying trends that conventional time-intensive scientific prediction systems may miss.

“The AI performs much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex meteorologist.

“What this hurricane season has demonstrated in short order is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the slower traditional weather models we’ve traditionally leaned on,” he said.

Clarifying AI Technology

To be sure, the system is an example of machine learning – a technique that has been used in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

AI training takes 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 do so on a standard PC – in sharp difference to the primary systems that governments have used for decades that can require many hours to run and need some of the biggest high-performance systems in the world.

Professional Responses and Future Advances

Nevertheless, the reality that Google’s model could exceed previous top-tier traditional systems so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a former expert. “The data is now large enough that it’s evident this is not a case of chance.”

Franklin said that while the AI is outperforming all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity predictions inaccurate. It had difficulty with another storm previously, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

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

“A key concern that troubles me is that while these predictions appear really, really good, the output of the model is kind of a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a private, for-profit company that has developed a high-performance forecasting system which grants experts a view of its techniques – unlike most other models which are offered free to the general audience in their entirety by the authorities 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 previous non-AI versions.

Future developments in artificial intelligence predictions appear to involve new firms tackling formerly tough-to-solve problems such as long-range forecasts and better early alerts of tornado outbreaks and flash flooding – and they have secured federal support to do so. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the national monitoring system.