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When Tropical Storm Melissa was churning south of Haiti, meteorologist Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the lead forecaster on duty, he forecasted that in just 24 hours the storm would become a severe hurricane and begin a turn towards the Jamaican shoreline. No forecaster had ever issued such a bold prediction for quick intensification.

But, Papin possessed a secret advantage: 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 evolved into a storm of astonishing strength that tore through Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are heavily relying upon the AI system. During 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his certainty: “Approximately 40/50 AI simulation runs show Melissa becoming a Category 5 hurricane. While I am not ready to predict that strength yet due to track uncertainty, that remains a possibility.

“There is a high probability that a phase of rapid intensification will occur as the system drifts over exceptionally hot ocean waters which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Conventional Systems

The AI model is the first artificial intelligence system dedicated to hurricanes, and currently the first to beat standard weather forecasters at their own game. Through all 13 Atlantic storms so far this year, Google’s model is the best – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 strength, among the most powerful coastal impacts recorded in nearly two centuries of data collection across the region. The confident prediction likely gave people in Jamaica additional preparation time to prepare for the catastrophe, potentially preserving lives and property.

The Way Google’s Model Functions

The AI system operates through spotting patterns that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their traditional counterparts, and the computing power is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are on par with and, in certain instances, superior than the less rapid physics-based forecasting tools we’ve traditionally leaned on,” he added.

Clarifying AI Technology

It’s important to note, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like weather science for years – and is not generative AI like ChatGPT.

Machine learning processes large datasets and pulls out patterns from them in a such a way that its system only takes a few minutes to come up with an result, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for years that can require many hours to process and need the largest high-performance systems in the world.

Expert Reactions and Future Advances

Nevertheless, the fact that Google’s model could exceed earlier top-tier traditional systems so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to predict the most intense storms.

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

Franklin noted that although the AI is outperforming all other models on predicting the trajectory of storms worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with another storm previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he intends to discuss with the company about how it can make the DeepMind output more useful for experts by providing extra under-the-hood data they can utilize to assess the reasons it is coming up with its conclusions.

“The one thing that troubles me is that while these predictions seem to be highly accurate, the output of the system is essentially a opaque process,” remarked Franklin.

Broader Sector Trends

There has never been a commercial entity that has developed a top-level weather model which allows researchers a peek into its methods – unlike nearly all systems which are provided at no cost to the general audience in their entirety by the governments that created and operate them.

Google is not the only one in starting to use AI to address difficult meteorological problems. The US and European governments are developing their respective AI weather models in the works – which have also shown better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of tornado outbreaks and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own atmospheric sensors to address deficiencies in the US weather-observing network.