🔗 Share this article

When Developing Cyclone Melissa was churning south of Haiti, weather expert Philippe Papin felt certain it was about to grow into a major tropical system.

As the lead forecaster on duty, he predicted that in a single day the storm would become a category 4 hurricane and begin a turn in the direction of the coast of Jamaica. Not a single expert had ever issued this confident forecast for quick intensification.

However, Papin had an ace up his sleeve: artificial intelligence in the form of the tech giant’s recently introduced DeepMind hurricane model – launched for the first time in June. True to the forecast, Melissa did become a system of remarkable power that ravaged Jamaica.

Increasing Dependence on Artificial Intelligence Predictions

Forecasters are increasingly leaning hard on Google DeepMind. On the morning of 25 October, Papin explained in his public discussion that Google’s model was a primary reason for his confidence: “Approximately 40/50 Google DeepMind simulation runs show Melissa reaching a Category 5 storm. While I am not ready to predict that intensity yet given path variability, that is still plausible.

“It appears likely that a period of quick strengthening is expected as the storm drifts over exceptionally hot ocean waters which is the highest oceanic heat content in the entire Atlantic basin.”

Outperforming Conventional 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 this season, the AI is top-performing – even beating human forecasters on track predictions.

The hurricane eventually made landfall in Jamaica at category 5 intensity, among the most powerful coastal impacts ever documented in almost 200 years of data collection across the Atlantic basin. The confident prediction probably provided residents extra time to prepare for the catastrophe, possibly saving people and assets.

How Google’s System Works

The AI system works by spotting patterns that traditional time-intensive scientific weather models may overlook.

“They do it much more quickly than their traditional counterparts, and the processing requirements is less expensive and demanding,” said Michael Lowry, a former meteorologist.

“What this hurricane season has proven in short order is that the recent artificial intelligence systems are competitive with and, in certain instances, more accurate than the less rapid physics-based weather models we’ve traditionally leaned on,” he said.

Understanding Machine Learning

To be sure, Google DeepMind is an instance of AI training – a method that has been employed in data-heavy sciences like weather science for years – and is distinct from creative artificial intelligence like ChatGPT.

AI training takes large datasets and pulls out patterns from them in a manner that its model only takes a few minutes to come up with an answer, and can do so on a standard PC – in sharp difference to the primary systems that governments have utilized for decades that can require many hours to run and require some of the biggest supercomputers in the world.

Professional Reactions and Upcoming Advances

Still, the reality that Google’s model could exceed previous top-tier legacy models so quickly is nothing short of amazing to meteorologists who have dedicated their lives trying to forecast the most intense storms.

“It’s astonishing,” said James Franklin, a former forecaster. “The data is sufficient that it’s evident this is not just beginner’s luck.”

He said that although the AI is beating all other models on forecasting the future path of hurricanes worldwide this year, like many AI models it sometimes errs on extreme strength predictions wrong. It had difficulty with another storm earlier this year, as it was similarly experiencing rapid intensification to category 5 above the Caribbean.

During the next break, Franklin said he intends to discuss with Google about how it can enhance the AI results even more helpful for forecasters by providing additional internal information they can utilize to evaluate exactly why it is coming up with its conclusions.

“The one thing that nags at me is that although these predictions seem to be really, really good, the results of the system is essentially a black box,” remarked Franklin.

Wider Industry Trends

There has never been a commercial entity that has produced a top-level forecasting system which grants experts a peek into its techniques – unlike most other models which are offered at no cost to the general audience in their full form by the authorities that created and operate them.

Google is not the only one in adopting artificial intelligence to solve difficult weather forecasting problems. The US and European governments also have their own artificial intelligence systems in the works – which have also shown improved skill over earlier non-AI versions.

Future developments in AI weather forecasts appear to involve startup companies taking swings at formerly difficult problems such as sub-seasonal outlooks and better early alerts of severe weather and sudden deluges – and they have secured US government funding to do so. One company, WindBorne Systems, is even deploying its proprietary weather balloons to address deficiencies in the national monitoring system.