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When Developing Cyclone Melissa was churning off the coast of Haiti, meteorologist Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the weather system would intensify into a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had previously made this confident forecast for quick intensification.

However, Papin possessed a secret advantage: AI technology in the guise of the tech giant’s recently introduced DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of astonishing strength that ravaged Jamaica.

Increasing Reliance on AI Predictions

Meteorologists are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin clarified in his official briefing that the AI tool was a primary reason for his confidence: “Roughly 40/50 Google DeepMind simulation runs show Melissa becoming a Category 5 storm. While I am unprepared to predict that strength at this time given path variability, that is still plausible.

“There is a high probability that a period of rapid intensification will occur as the storm drifts over exceptionally hot sea temperatures which represent the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first AI model dedicated to hurricanes, and currently the first to outperform standard weather forecasters at their own game. Through all tropical systems this season, the AI is the best – surpassing experts on track predictions.

Melissa ultimately struck in Jamaica at maximum intensity, among the most powerful landfalls ever documented in nearly two centuries of data collection across the Atlantic basin. Papin’s bold forecast likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving lives and property.

The Way The Model Works

The AI system works by identifying trends that traditional lengthy physics-based prediction systems may miss.

“The AI performs far faster than their traditional counterparts, and the computing power is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in short order is that the newcomer artificial intelligence systems are competitive with and, in some cases, more accurate than the slower physics-based weather models we’ve traditionally leaned on,” he added.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an example of AI training – a technique that has been used in research fields 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 result, and can operate on a desktop computer – in strong contrast to the flagship models that authorities have utilized for decades that can take hours to process and need the largest high-performance systems in the world.

Professional Responses and Future Developments

Still, the reality that the AI could outperform previous gold-standard legacy models so rapidly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The sample is now large enough that it’s evident this is not just chance.”

He noted that while the AI is beating all other models on forecasting the trajectory of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It had difficulty with Hurricane Erin earlier this year, as it was also undergoing rapid intensification to category 5 north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can enhance the DeepMind output even more helpful for forecasters by offering extra under-the-hood data they can use to assess exactly why it is coming up with its answers.

“The one thing that nags at me is that while these predictions seem to be really, really good, the results of the model is kind of a opaque process,” remarked Franklin.

Wider Industry Developments

There has never been a commercial entity that has developed a high-performance weather model which grants experts a view of its methods – unlike nearly all other models which are offered free to the public in their entirety by the governments that designed and maintain them.

Google is not alone in adopting AI to address difficult weather forecasting problems. The authorities are developing their own artificial intelligence systems in the works – which have demonstrated better performance over earlier traditional systems.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and better early alerts of tornado outbreaks and sudden deluges – and they have secured federal support to pursue this. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to address deficiencies in the national monitoring system.