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As Tropical Storm Melissa was churning off the coast of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

Serving as lead forecaster on duty, he forecasted that in just 24 hours the storm would become a category 4 hurricane and start shifting towards the Jamaican shoreline. Not a single expert had previously made such a bold prediction for rapid strengthening.

But, Papin possessed a secret advantage: artificial intelligence in the form of the tech giant’s new DeepMind cyclone prediction system – released for the first time in June. And, as predicted, Melissa evolved into a storm of remarkable power that tore through Jamaica.

Growing Dependence on Artificial Intelligence Forecasting

Meteorologists are heavily relying upon the AI system. During 25 October, Papin explained in his public discussion that Google’s model was a key factor for his certainty: “Roughly 40/50 AI ensemble members show Melissa reaching a most intense hurricane. Although I am unprepared to predict that intensity at this time due to path variability, that remains a possibility.

“There is a high probability that a period of rapid intensification is expected as the system drifts over exceptionally hot sea temperatures which is the highest marine thermal energy in the entire Atlantic basin.”

Surpassing Traditional Systems

The AI model is the first artificial intelligence system dedicated to tropical cyclones, and now the first to beat standard weather forecasters at their own game. Across all 13 Atlantic storms so far this year, the AI is the best – surpassing human forecasters on path forecasts.

Melissa ultimately struck in Jamaica at category 5 strength, one of the strongest landfalls recorded in almost 200 years of record-keeping across the Atlantic basin. The confident prediction likely gave residents extra time to get ready for the catastrophe, possibly saving lives and property.

The Way Google’s System Works

The AI system operates through identifying trends that traditional time-intensive scientific weather models may overlook.

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

“What this hurricane season has demonstrated in quick time is that the recent AI weather models are competitive with and, in certain instances, superior than the less rapid traditional forecasting tools we’ve traditionally leaned on,” he added.

Understanding AI Technology

To be sure, Google DeepMind is an example of machine learning – a technique that has been employed in research fields like meteorology for a long time – 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 model only takes a few minutes to generate an answer, and can operate on a desktop computer – in strong contrast to the primary systems that governments have used for years that can take hours to run and need some of the biggest supercomputers in the world.

Expert Responses and Upcoming Advances

Nevertheless, the reality that the AI could outperform earlier gold-standard legacy models so rapidly is nothing short of amazing to meteorologists who have spent their careers trying to predict the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is sufficient that it’s pretty clear this is not just beginner’s luck.”

Franklin noted that while Google DeepMind is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it occasionally gets high-end intensity predictions inaccurate. It had difficulty with Hurricane Erin previously, as it was similarly experiencing quick strengthening to category 5 north of the Caribbean.

In the coming offseason, Franklin said he plans to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering extra internal information they can use to assess exactly why it is producing its conclusions.

“The one thing that nags at me is that while these forecasts seem to be really, really good, the output of the model is essentially a opaque process,” remarked Franklin.

Wider Sector Trends

Historically, no a private, for-profit company that has developed a high-performance weather model which allows researchers a view of its techniques – in contrast to nearly all other models 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 artificial intelligence to address difficult meteorological problems. The US and European governments are developing their respective artificial intelligence systems in the development phase – which have also shown better performance over earlier non-AI versions.

Future developments in artificial intelligence predictions seem to be startup companies tackling previously difficult problems such as sub-seasonal outlooks and better advance warnings of severe weather and flash flooding – and they are receiving federal support to pursue this. A particular firm, WindBorne Systems, is even deploying its own weather balloons to fill the gaps in the national monitoring system.