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When Developing Cyclone Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain 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 category 4 hurricane and begin a turn towards the coast of Jamaica. Not a single expert had previously made this confident prediction for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. And, as predicted, Melissa evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on AI Predictions

Meteorologists are heavily relying upon the AI system. During 25 October, Papin clarified in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI ensemble members indicate Melissa reaching a most intense hurricane. Although I am unprepared to forecast that strength yet due to path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the system moves slowly over very warm ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Surpassing Traditional Models

Google DeepMind is the pioneer AI model focused on tropical cyclones, and now the initial to outperform standard meteorological experts at their specialty. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on track predictions.

Melissa eventually made landfall in Jamaica at category 5 intensity, one of the strongest coastal impacts ever documented in nearly two centuries of data collection across the Atlantic basin. The confident prediction likely gave people in Jamaica extra time to get ready for the catastrophe, possibly saving people and assets.

How Google’s System Works

Google’s model operates through 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 more affordable and demanding,” said Michael Lowry, a ex meteorologist.

“This season’s events has demonstrated in short order is that the newcomer AI weather models are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve relied upon,” he said.

Clarifying AI Technology

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

Machine learning takes large datasets and extracts trends from them in a manner that its model only requires minutes to come up with an answer, and can do so on a desktop computer – in strong contrast to the primary systems that governments have utilized for years that can take hours to run and need some of the biggest supercomputers in the world.

Professional Reactions and Future Developments

Still, the reality that Google’s model could outperform previous gold-standard legacy models so rapidly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the world’s strongest weather systems.

“It’s astonishing,” said James Franklin, a retired forecaster. “The sample is sufficient that it’s evident this is not just chance.”

He noted that while the AI is outperforming all other models on forecasting the trajectory of hurricanes globally this year, like many AI models it sometimes errs on extreme strength forecasts inaccurate. It had difficulty with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin said he intends to talk with Google about how it can enhance the AI results more useful for forecasters by providing extra internal information they can utilize to evaluate exactly why it is coming up with its answers.

“A key concern that nags at me is that although these predictions seem to be highly accurate, the output of the model is essentially a opaque process,” said Franklin.

Broader Sector Developments

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a peek into its techniques – in contrast to nearly all systems which are offered free to the public in their full form by the governments that created and operate them.

Google is not alone in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments are developing their own AI weather models in the works – which have demonstrated better performance over earlier non-AI versions.

Future developments in AI weather forecasts seem to be new firms tackling formerly difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving federal support to do so. One company, WindBorne Systems, is also deploying its proprietary atmospheric sensors to fill the gaps in the national monitoring system.