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As Tropical Storm Melissa swirled south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a monster hurricane.

Serving as primary meteorologist on duty, he forecasted that in a single day the storm would intensify into a category 4 hurricane and start shifting in the direction of the coast of Jamaica. No forecaster had ever issued such a bold forecast for rapid strengthening.

However, Papin had an ace up his sleeve: artificial intelligence in the form of Google’s recently introduced DeepMind cyclone prediction system – released for the initial occasion in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Growing Reliance on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his official briefing that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind ensemble members show Melissa becoming a most intense storm. While I am unprepared to predict that strength yet given path variability, that is still plausible.

“There is a high probability that a period of quick strengthening will occur as the system moves slowly over exceptionally hot sea temperatures which represent the highest oceanic heat content in the entire Atlantic basin.”

Surpassing Conventional Models

Google DeepMind is the first artificial intelligence system dedicated to hurricanes, and currently the initial to outperform traditional meteorological experts at their own game. Across all tropical systems so far this year, Google’s model is top-performing – surpassing experts on track predictions.

The hurricane eventually made landfall in Jamaica at maximum intensity, 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 disaster, possibly saving lives and property.

How The Model Works

The AI system works by identifying trends that traditional lengthy physics-based prediction systems may miss.

“They do it far faster than their physics-based cousins, and the processing requirements is less expensive and time consuming,” stated Michael Lowry, a ex forecaster.

“What this hurricane season has demonstrated in quick time is that the recent artificial intelligence systems are competitive with and, in some cases, superior than the less rapid traditional weather models we’ve traditionally leaned on,” he 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 years – and is distinct from creative artificial intelligence like ChatGPT.

Machine learning takes mounds of data and extracts trends from them in a such a way that its model only takes a few minutes to generate an answer, and can do so on a desktop computer – in sharp difference to the primary systems that governments have used for years that can require many hours to run and need the largest supercomputers in the world.

Expert Responses and Future Developments

Still, the fact that the AI could outperform earlier gold-standard traditional systems so quickly is truly remarkable to meteorologists who have spent their careers trying to forecast the world’s strongest storms.

“I’m impressed,” said James Franklin, a former forecaster. “The data is now large enough that it’s pretty clear this is not just chance.”

Franklin said that although Google DeepMind is outperforming all competing systems on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength forecasts wrong. It struggled with Hurricane Erin earlier this year, as it was similarly experiencing rapid intensification to maximum intensity north of the Caribbean.

In the coming offseason, Franklin stated he intends to talk with Google about how it can make the DeepMind output even more helpful for forecasters by offering additional internal information they can use to evaluate exactly why it is coming up with its answers.

“A key concern that troubles me is that although these predictions appear really, really good, the output of the model is kind of a opaque process,” said Franklin.

Wider Sector Developments

There has never been a commercial entity that has developed a high-performance forecasting system which allows researchers a view of its methods – unlike most systems which are offered at no cost to the general audience in their entirety by the governments that designed and maintain them.

The company is not the only one in starting to use artificial intelligence to address difficult meteorological problems. The US and European governments also have their own artificial intelligence systems in the works – which have demonstrated improved skill over previous traditional systems.

Future developments in AI weather forecasts appear to involve startup companies tackling formerly tough-to-solve problems such as sub-seasonal outlooks and improved advance warnings of severe weather and flash flooding – and they have secured US government funding to pursue this. One company, WindBorne Systems, is even launching its own atmospheric sensors to fill the gaps in the US weather-observing network.