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As Tropical Storm Melissa swirled south of Haiti, meteorologist Philippe Papin felt certain it would soon escalate to a monster hurricane.

As the primary meteorologist on duty, he predicted that in a single day the weather system would intensify into a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had ever issued such a bold forecast for rapid strengthening.

But, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s new DeepMind hurricane model – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that ravaged Jamaica.

Increasing Dependence on AI Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that the AI tool was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa reaching a most intense hurricane. Although I am unprepared to predict that intensity yet given track uncertainty, that remains a possibility.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot sea temperatures which represent 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 now the first to outperform traditional meteorological experts at their own game. Across all 13 Atlantic storms so far this year, Google’s model is the best – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls ever documented in nearly two centuries of record-keeping across the region. The confident prediction probably provided people in Jamaica extra time to prepare for the catastrophe, possibly saving lives and property.

The Way The System Functions

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

“The AI performs far faster than their traditional counterparts, and the processing requirements is more affordable and demanding,” said Michael Lowry, a former forecaster.

“What this hurricane season has demonstrated in quick time is that the newcomer AI weather models are competitive with and, in some cases, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Understanding Machine Learning

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

Machine learning takes mounds of data and pulls out patterns from them in a such a way that its model only takes a few minutes to generate an result, and can do so on a standard PC – in strong contrast to the flagship models that governments have used for decades that can take hours to run and require the largest high-performance systems in the world.

Expert Responses and Future Advances

Still, the reality that Google’s model could outperform previous top-tier traditional systems so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to predict the most intense weather systems.

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

Franklin noted that while the AI is outperforming all other models on predicting the future path of hurricanes worldwide this year, similar to other systems it occasionally gets high-end intensity forecasts wrong. It struggled with Hurricane Erin previously, as it was also undergoing rapid intensification to maximum intensity north of the Caribbean.

During the next break, Franklin said he plans to discuss with Google about how it can enhance the AI results more useful for forecasters by offering additional internal information they can use to assess exactly why it is coming up with its answers.

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

Broader Sector Developments

There has never been a commercial entity that has developed a top-level forecasting system which allows researchers a view of its methods – in contrast to most systems which are offered free to the public in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use AI to address challenging meteorological problems. The authorities also have their respective artificial intelligence systems in the development phase – which have demonstrated better performance over previous traditional systems.

Future developments in artificial intelligence predictions seem to be startup companies taking swings at formerly tough-to-solve problems such as long-range forecasts and better advance warnings of severe weather and sudden deluges – and they have secured US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to address deficiencies in the national monitoring system.