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As Developing Cyclone Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it would soon grow into a major tropical system.

As the lead forecaster on duty, he forecasted that in a single day the storm would become a severe hurricane and start shifting towards the coast of Jamaica. No forecaster had ever issued such a bold prediction for quick intensification.

But, Papin had an ace up his sleeve: AI technology in the form of Google’s new DeepMind hurricane model – launched for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Dependence on AI Forecasting

Forecasters are increasingly leaning hard on the AI system. On the morning of 25 October, Papin clarified in his public discussion that the AI tool was a primary reason for his certainty: “Approximately 40/50 AI ensemble members show Melissa becoming a Category 5 hurricane. Although I am unprepared to forecast that intensity at this time due to path variability, that remains a possibility.

“There is a high probability that a phase of rapid intensification is expected as the storm drifts over exceptionally hot ocean waters which represent the most extreme oceanic heat content in the whole Atlantic basin.”

Outperforming Conventional Systems

The AI model is the pioneer artificial intelligence system focused on tropical cyclones, and now the first to outperform standard meteorological experts at their specialty. Through all 13 Atlantic storms so far this year, Google’s model is top-performing – surpassing experts on path forecasts.

The hurricane eventually made landfall in Jamaica at maximum strength, among the most powerful landfalls ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents additional preparation time to get ready for the catastrophe, possibly saving people and assets.

How The System Functions

Google’s model operates through spotting patterns that conventional lengthy physics-based prediction systems may miss.

“They do it much more quickly than their traditional counterparts, and the computing power is less expensive and demanding,” said Michael Lowry, a ex forecaster.

“This season’s events has proven in quick time is that the newcomer AI weather models are on par with and, in some cases, superior than the less rapid physics-based weather models we’ve traditionally leaned on,” Lowry said.

Clarifying Machine Learning

It’s important to note, Google DeepMind is an instance of machine learning – a method that has been employed in data-heavy sciences like weather science for a long time – and is distinct from generative AI like ChatGPT.

Machine learning takes large datasets and extracts trends from them in a such a way that its system only requires minutes to come up with an answer, and can operate on a desktop computer – in sharp difference to the primary systems that governments have utilized for years that can take hours to process and require some of the biggest high-performance systems in the world.

Expert Responses and Future Advances

Still, the reality that Google’s model could exceed earlier gold-standard legacy models so quickly is nothing short of amazing to weather scientists who have dedicated their lives trying to forecast the most intense weather systems.

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

Franklin said that while Google DeepMind is outperforming 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 Hurricane Erin earlier this year, as it was also undergoing quick strengthening to category 5 above the Caribbean.

During the next break, Franklin said he plans to discuss with the company about how it can enhance the AI results even more helpful for forecasters by providing extra under-the-hood data they can use to assess exactly why it is coming up with its conclusions.

“The one thing that troubles me is that although these predictions appear highly accurate, the results of the model is kind of a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level weather model which grants experts a peek into its techniques – unlike most systems which are provided at no cost to the public in their entirety by the governments that created and operate them.

Google is not the only one in starting to use artificial intelligence to solve difficult meteorological problems. The US and European governments also have their respective AI weather models in the development phase – which have also shown improved skill over earlier non-AI versions.

The next steps in artificial intelligence predictions appear to involve startup companies taking swings at previously tough-to-solve problems such as sub-seasonal outlooks and better advance warnings of severe weather and sudden deluges – and they are receiving US government funding to pursue this. A particular firm, WindBorne Systems, is also launching its proprietary weather balloons to address deficiencies in the US weather-observing network.