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When Tropical Storm Melissa swirled off the coast of Haiti, meteorologist Philippe Papin had confidence it was about to grow into a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the weather system would become a severe hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued this confident prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence 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 did become a system of remarkable power that tore through Jamaica.

Growing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin clarified in his official briefing that the AI tool was a key factor for his certainty: “Roughly 40/50 Google DeepMind ensemble members indicate Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that strength at this time due to track uncertainty, that remains a possibility.

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

Outperforming Traditional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and now the first to beat standard meteorological experts at their own game. Through all tropical systems this season, Google’s model is top-performing – even beating human forecasters on path forecasts.

Melissa eventually made landfall in Jamaica at maximum intensity, among the most powerful coastal impacts recorded in almost 200 years of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to get ready for the catastrophe, potentially preserving people and assets.

The Way Google’s System Functions

The AI system operates through identifying trends that traditional lengthy physics-based weather models may miss.

“The AI performs far faster than their physics-based cousins, and the computing power is more affordable and time consuming,” said Michael Lowry, a ex forecaster.

“What this hurricane season has proven in quick time is that the newcomer AI weather models are on par with and, in certain instances, more accurate than the slower traditional weather models we’ve traditionally leaned on,” he added.

Understanding Machine Learning

It’s important to note, the system is an instance of machine learning – a method that has been used in data-heavy sciences like meteorology for years – and is distinct from generative AI like ChatGPT.

AI training processes large datasets and pulls out patterns from them in a such a way that its system only requires minutes to come up with an answer, and can do so on a desktop computer – in sharp difference to the flagship models that governments have used for years that can take hours to run and require some of the biggest high-performance systems in the world.

Expert Reactions and Future Developments

Nevertheless, the fact that Google’s model could outperform previous gold-standard traditional systems so quickly is nothing short of amazing to weather scientists who have spent their careers trying to forecast the world’s strongest weather systems.

“I’m impressed,” said James Franklin, a retired forecaster. “The sample is now large enough that it’s pretty clear this is not a case of beginner’s luck.”

Franklin said that although the AI is beating all other models on predicting the future path of storms globally this year, like many AI models it sometimes errs on high-end intensity predictions wrong. It struggled with another storm earlier this year, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin stated he plans to talk with the company about how it can make the DeepMind output even more helpful for forecasters by providing additional internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these forecasts seem to be highly accurate, the output of the system is essentially a black box,” remarked Franklin.

Wider Industry Developments

Historically, no a commercial entity that has developed a top-level forecasting system which allows researchers 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 the only one in starting to use artificial intelligence to solve challenging weather forecasting problems. The authorities are developing their respective AI weather models in the development phase – which have also shown better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be new firms tackling formerly tough-to-solve problems such as sub-seasonal outlooks and better early alerts of severe weather and flash flooding – and they are receiving federal support to pursue this. One company, WindBorne Systems, is also deploying its own weather balloons to fill the gaps in the national monitoring system.