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When Developing Cyclone Melissa swirled off the coast of Haiti, weather expert Philippe Papin had confidence it was about to escalate to a monster hurricane.

As the primary meteorologist on duty, he forecasted that in a single day the weather system would intensify into a category 4 hurricane and begin a turn towards the Jamaican shoreline. Not a single expert had previously made this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: AI technology in the guise of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. And, as predicted, Melissa did become a storm of remarkable power that tore through Jamaica.

Increasing Dependence on Artificial Intelligence Forecasting

Forecasters are increasingly leaning hard on Google DeepMind. During 25 October, Papin explained in his official briefing that Google’s model was a key factor for his confidence: “Roughly 40/50 Google DeepMind ensemble members show Melissa becoming a most intense hurricane. Although I am not ready to forecast that strength yet due to track uncertainty, that is still plausible.

“It appears likely that a phase of quick strengthening is expected as the system drifts over exceptionally hot ocean waters which is the most extreme marine thermal energy in the whole Atlantic basin.”

Surpassing Traditional Systems

The AI model is the pioneer AI model focused on tropical cyclones, and currently the initial to outperform traditional weather forecasters at their own game. Through all tropical systems so far this year, the AI is top-performing – even beating experts on track predictions.

Melissa ultimately struck in Jamaica at category 5 intensity, one of the strongest landfalls ever documented in almost 200 years of record-keeping across the Atlantic basin. The confident prediction probably provided residents additional preparation time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Functions

The AI system operates through spotting patterns that conventional lengthy scientific weather models may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is more affordable and time consuming,” stated Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the recent AI weather models are competitive with and, in some cases, superior than the slower traditional forecasting tools we’ve traditionally leaned on,” Lowry added.

Clarifying AI Technology

To be sure, Google DeepMind is an example of machine learning – a method that has been employed in research fields like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes mounds of data and pulls out patterns from them in a manner that its model only takes a few minutes to generate an answer, and can operate on a standard PC – in strong contrast to the flagship models that governments have utilized for decades that can require many hours to process and require the largest supercomputers in the world.

Expert Reactions and Future Advances

Still, the reality that Google’s model could exceed previous top-tier 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 retired forecaster. “The sample is now large enough that it’s evident this is not a case of chance.”

He noted that while Google DeepMind is beating all competing systems on predicting the trajectory of hurricanes worldwide this year, similar to other systems it occasionally gets extreme strength forecasts inaccurate. It struggled with another storm earlier this year, as it was also undergoing rapid intensification to maximum intensity above the Caribbean.

In the coming offseason, he said he plans to discuss with the company about how it can make the AI results more useful for experts by offering additional under-the-hood data they can use to evaluate the reasons it is coming up with its conclusions.

“A key concern that nags at me is that although these forecasts appear really, really good, the output of the model is kind of a black box,” said Franklin.

Broader Industry Trends

There has never been a private, for-profit company that has developed a high-performance weather model which grants experts a view of its methods – unlike most other models which are provided free to the public in their entirety by the authorities that designed and maintain them.

Google is not the only one in starting to use AI to address challenging meteorological problems. The US and European governments are developing their own AI weather models in the development phase – which have demonstrated better performance over earlier non-AI versions.

The next steps in artificial intelligence predictions seem to be new firms tackling previously difficult problems such as long-range forecasts and improved advance warnings of tornado outbreaks and flash flooding – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its own weather balloons to fill the gaps in the US weather-observing network.