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When Developing Cyclone Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it would soon grow into a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for quick intensification.

However, Papin possessed a secret advantage: artificial intelligence in the guise of Google’s new DeepMind hurricane model – released for the first time in June. True to the forecast, Melissa did become a storm of astonishing strength that tore through Jamaica.

Increasing Reliance on Artificial Intelligence Predictions

Meteorologists are heavily relying upon Google DeepMind. During 25 October, Papin clarified in his public discussion that Google’s model was a primary reason for his confidence: “Roughly 40/50 AI ensemble members show Melissa reaching a Category 5 storm. While I am unprepared to predict that intensity yet given 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.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on hurricanes, and currently the first to beat traditional meteorological experts at their own game. Through all 13 Atlantic storms so far this year, the AI is top-performing – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at maximum strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the disaster, possibly saving people and assets.

The Way The Model Works

Google’s model works by identifying trends that conventional lengthy scientific weather models may overlook.

“The AI performs much more quickly than their physics-based cousins, and the computing power is more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the slower physics-based forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying Machine Learning

It’s important to note, the system is an example of AI training – a technique that has been used in data-heavy sciences like weather science for years – and is not generative AI like ChatGPT.

AI training takes mounds of data and extracts trends from them in a such a way that its model only requires minutes to generate an answer, and can operate on a standard PC – in sharp difference to the flagship models that governments have utilized for years that can require many hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Future Advances

Still, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is truly remarkable to weather scientists who have spent their careers trying to predict the world’s strongest weather systems.

“It’s astonishing,” commented James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just chance.”

Franklin noted that although Google DeepMind is beating all other models on predicting the trajectory of hurricanes globally this year, like many AI models it occasionally gets extreme strength forecasts inaccurate. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to category 5 north of the Caribbean.

During the next break, he said he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by providing extra under-the-hood data they can use to evaluate exactly why it is producing its answers.

“A key concern that nags at me is that while these forecasts appear highly accurate, the output of the model is kind of a black box,” remarked Franklin.

Broader Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which allows researchers a view of its techniques – unlike most other models which are provided at no cost to the public in their full form by the authorities that designed and maintain them.

Google is not alone in starting to use artificial intelligence to solve difficult weather forecasting problems. The US and European governments are developing their own AI weather models in the development phase – which have also shown better performance over earlier traditional systems.

The next steps in AI weather forecasts appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to do so. A particular firm, WindBorne Systems, is also deploying its proprietary weather balloons to address deficiencies in the national monitoring system.